ai data engineer

Edge

<div>
 
 
 About Edge
 
 
 
 Edge helps medical, dental, and insurance organizations scale faster and operate more efficiently by providing pre-vetted, global talent to handle mission-critical administrative work: documentation, scheduling, billing, claims, and more. We combine rigorous training with a people-first approach, so our clients can focus on customer experiences while we handle the rest.
 
 
 We combine data-driven sourcing strategies with deep industry expertise to deliver results that go beyond resumes. We&rsquo;re now expanding our sales development team to accelerate growth and that&rsquo;s where you come in.
 
 
 
 
 
 
 About the role:
 
 
 
 We are hiring an AI &amp; Data Engineer to design and deploy AI-powered business systems across sales, operations, HR, and compliance. You will report to the Manager, Data Platform and work within a small, high-impact team with direct visibility to the CEO.
 
 
 This role spans the full modern data + AI stack: building reliable data pipelines, owning product and marketing analytics (GA4), developing agentic AI workflows, and integrating systems across the business. It is suited for someone who operates at the intersection of data engineering, AI systems, and business execution&mdash;capable of building pipelines, shipping LLM-powered agents, and connecting everything into production systems that drive outcomes.
 
 
 
 
 
 
 What you'll do:
 
 
 <ul>
 <li>
 Build and maintain scalable data pipelines, ETL/ELT workflows, and warehouse/lakehouse data models that power analytics and AI systems, ensuring data quality, observability, and reliability across production flows
 </li>
 <li>
 Own GA4 end-to-end: event schema design, custom dimensions/metrics, conversion tracking, GTM (including server-side where applicable), and measurement strategy
 </li>
 <li>
 Pipe GA4 data into BigQuery and the warehouse; unify clickstream, product, CRM, operational, and financial data into a consistent view of the customer journey
 </li>
 <li>
 Partner with Marketing, Growth, and Product to define KPIs, funnels, attribution models, and audience segmentation; build dashboards and reporting that drive decision-making
 </li>
 <li>
 Maintain tracking governance including naming conventions, data layer standards, documentation, and privacy/consent compliance (e.g., GDPR-aligned practices)
 </li>
 <li>
 Design, build, and deploy agentic AI systems using orchestration frameworks (LangGraph, LlamaIndex, CrewAI, etc.), including RAG pipelines, tool use, memory, evaluation, and production LLM workflows
 </li>
 <li>
 Develop automation across core business functions (sales, operations, HR, compliance), including outbound sales workflows, account management, client health tracking, talent pipeline optimization, and market signal monitoring
 </li>
 <li>
 Integrate systems across Salesforce, HubSpot, Slack, email, GA4/GTM, and internal platforms using APIs, webhooks, and event-driven architectures
 </li>
 <li>
 Build and maintain reliable connectors and sync processes; monitor integrations, handle failures gracefully, and implement alerting and recovery mechanisms
 </li>
 <li>
 Support strategic and internal initiatives, including expansion efforts, workflow monitoring systems, and data-driven insight generation across business operations
 </li>
 </ul>
 
 
 
 
 
 What you'll need:
 
 
 <ul>
 <li>
 Strong data engineering fundamentals: pipelines, ETL/ELT, data modeling, and warehouse/lakehouse architectures
 </li>
 <li>
 Hands-on experience with GA4 (event design, GTM configuration, BigQuery export, and production-grade reporting)
 </li>
 <li>
 Experience building agentic AI systems (orchestration frameworks, RAG, tool use, LLM workflows in production environments)
 </li>
 <li>
 Solid understanding of APIs, integrations, and event-driven system design
 </li>
 <li>
 Proficiency in Python and SQL
 </li>
 <li>
 Experience with cloud infrastructure (AWS, GCP, or similar); BigQuery strongly preferred
 </li>
 <li>
 Experience working with business systems (e.g., Salesforce, HubSpot, communication tools, dashboards)
 </li>
 <li>
 Strong execution ability: breaking down ambiguous problems, operating across data + business contexts, and delivering production systems (not prototypes)
 </li>
 <li>
 High ownership, bias for action, and strong communication across technical and non-technical stakeholders
 </li>
 </ul>
 
 
 
 
 
 Nice to have:
 
 server-side GTM, Consent Mode v2, privacy-compliant tracking; attribution modeling/marketing mix; dbt, Airflow/Dagster/Prefect; reverse ETL (Hightouch, Census); experience in high-compliance industries; PowerBI / MS Fabric
 
 
 
 
 
 
 Why Join Edge?
 
 
 
 Edge is at a pivotal growth point, and this role offers the rare opportunity to shape the future of global employment. Here, your work directly impacts our growth trajectory, empowers businesses, and creates global employment opportunities. We&rsquo;re not just offering a job; we&rsquo;re inviting you to be part of a revolution.
 
 
 Edge is proud to be an equal-opportunity employer. We are committed to fostering a diverse and inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, physical or mental disability, age, veteran status, or any other legally protected basis.
 
</div>

AI & Data Engineer

<div>
 
 
 Job Title: Agentic AI Data Engineer
 
 
 
 
 Hybrid: Jersey City,NJ
 
 
 
 
 Job Type: FullTime
 
 
 
 
 Max Budget : $150K
 
 
 
 
 Total Experience required : 5-10 Years
 
 
 
 
 
 
 Role Overview
 
 
 We are seeking a highly skilled Agentic AI Data Engineer to design, build, and optimize intelligent, autonomous data systems that power next-generation AI applications. This role blends data engineering, machine learning infrastructure, and emerging agent-based AI frameworks to enable scalable, self-orchestrating pipelines and decision-making systems.
 
 
 You will work at the intersection of data platforms, large language models (LLMs), and cloud-native architectures&mdash;building systems that can reason, act, and adapt autonomously.
 
 
 ---
 
 
 Key Responsibilities
 
 
 &middot; Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines
 
 
 &middot; Build scalable data pipelines for structured and unstructured data (batch + real-time)
 
 
 &middot; Develop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks
 
 
 &middot; Integrate AWS AI/ML services into production-grade architectures
 
 
 &middot; Develop and optimize data lakes, warehouses, and lakehouse architectures
 
 
 &middot; Build APIs and microservices to expose AI/ML capabilities
 
 
 &middot; Ensure data quality, governance, and security across pipelines
 
 
 &middot; Collaborate with data scientists, ML engineers, and product teams to deploy AI solutions
 
 
 &middot; Implement monitoring, logging, and observability for AI agents and pipelines
 
 
 &middot; Optimize cost and performance of cloud-based AI workloads
 
 
 ---
 
 
 Required Technical Skills
 
 
 Cloud &amp; AWS Ecosystem
 
 
 &middot; Strong experience with AWS services, including:
 
 
 o Amazon S3, Glue, Lambda, Step Functions
 
 
 o Amazon Redshift / Athena
 
 
 o Amazon SageMaker (training, deployment, pipelines)
 
 
 o Amazon Bedrock (foundation models, agents, knowledge bases)
 
 
 AI/ML &amp; Agentic Systems
 
 
 &middot; Experience with LLMs and generative AI systems
 
 
 &middot; Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)
 
 
 &middot; Familiarity with AgentCore / agent orchestration platforms
 
 
 &middot; Understanding of RAG architectures, embeddings, and vector databases
 
 
 &middot; Experience with model deployment, inference optimization, and prompt engineering
 
 
 Data Engineering
 
 
 &middot; Strong proficiency in Python and SQL
 
 
 &middot; Experience with ETL/ELT tools and frameworks
 
 
 &middot; Distributed data processing (Spark, PySpark, or similar)
 
 
 &middot; Streaming technologies (Kafka, Kinesis, or similar)
 
 
 &middot; Data modeling and schema design
 
 
 Data &amp; AI Infrastructure
 
 
 &middot; Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch)
 
 
 &middot; Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)
 
 
 &middot; Containerization (Docker) and orchestration (Kubernetes)
 
 
 &middot; CI/CD for ML and data pipelines
 
 
 ---
 
 
 Preferred Qualifications
 
 
 &middot; Experience building autonomous AI agents for enterprise use cases
 
 
 &middot; Knowledge of multi-agent collaboration systems and planning algorithms
 
 
 &middot; Familiarity with LangChain, LlamaIndex, or similar frameworks
 
 
 &middot; Experience with MLOps and LLMOps practices
 
 
 &middot; Understanding of graph-based workflows and knowledge graphs
 
 
 &middot; Exposure to real-time AI systems and event-driven architectures
 
 
 ---
 
 
 Soft Skills
 
 
 &middot; Strong problem-solving and system design skills
 
 
 &middot; Ability to work in fast-paced, evolving AI environments
 
 
 &middot; Effective communication and cross-functional collaboration
 
 
 &middot; Curiosity and adaptability to emerging AI technologies
 
 
 ---
 
 
 Education &amp; Experience
 
 
 &middot; Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or related field
 
 
 &middot; 4+ years of experience in data engineering or ML engineering
 
 
 &middot; Hands-on experience with production-grade AI/ML systems
 
 
 ---
 
 
 Nice-to-Have
 
 
 &middot; Experience with reinforcement learning or planning systems
 
 
 &middot; Background in distributed systems design
 
 
 &middot; Contributions to open-source AI/data projects
 
 
 &middot; Certifications in AWS (e.g., Solutions Architect, Machine Learning Specialty)
 
 
 ---
 
 
 What You&rsquo;ll Build
 
 
 &middot; Autonomous data pipelines that self-heal and optimize
 
 
 &middot; AI agents capable of reasoning over enterprise data
 
 
 &middot; Scalable LLM-powered applications integrated with business workflows
 
 
 &middot; Intelligent systems that move beyond automation into decision-making
 
 
 
 
 
 
 
 
 For more information on benefits and what we offer please visit us at
 
 https://www.exlservice.com/us-careers-and-benefits
 
 
</div>

Agentic AI Data Engineer

Interact Technology Solutions

<div>
 
 Job Summary
 
 
 
 The
 
 Senior AI Data Engineer
 
 is responsible for designing, building, and optimizing enterprise-scale data and AI infrastructure to support machine learning models, generative AI applications, and real-time analytics. The role drives the development of end-to-end data pipelines, from ingestion to production-ready AI data products, ensuring scalability, performance, and compliance across multi-cloud environments.
 
 
 
 Accountability &amp; Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and maintain scalable ETL/ELT data pipelines using modern data engineering tools (e.g., Apache Spark, dbt).
 </li>
 <li>
 Architect and implement Lakehouse data platforms (Delta Lake, Apache Iceberg, Apache Hudi) following Medallion architecture (Bronze/Silver/Gold).
 </li>
 <li>
 Develop real-time streaming pipelines using Apache Kafka, Apache Flink, and Spark Structured Streaming.
 </li>
 <li>
 Build and optimize AI/GenAI data pipelines for LLM training, fine-tuning, and inference (tokenization, dataset curation, prompt engineering datasets).
 </li>
 <li>
 Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embedding workflows and vector database integration.
 </li>
 <li>
 Manage feature stores for real-time and batch machine learning use cases.
 </li>
 <li>
 Integrate data pipelines with AI/ML platforms (Databricks MLflow, Azure ML, AWS SageMaker, Vertex AI, OpenAI/Azure OpenAI).
 </li>
 <li>
 Implement data orchestration workflows using Apache Airflow or similar tools with CI/CD pipelines.
 </li>
 <li>
 Ensure data quality, governance, and security using frameworks such as Great Expectations and data catalog tools.
 </li>
 <li>
 Deploy and manage infrastructure using Infrastructure-as-Code tools (Terraform, Bicep, CDK).
 </li>
 <li>
 Collaborate with Data Scientists, ML Engineers, and Solution Architects to deliver production-ready AI solutions.
 </li>
 <li>
 Lead technical design decisions, mentor junior engineers, and contribute to data platform strategy.
 </li>
 <li>
 Maintain documentation, data contracts, and operational runbooks for all pipelines.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 
 1
 
 &ndash; Required Experience
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Engineering, or related field.
 </li>
 <li>
 4&ndash;5 years of experience in data engineering, with strong exposure to AI/ML data infrastructure.
 </li>
 <li>
 Proven experience building scalable data pipelines and working with large-scale datasets.
 </li>
 <li>
 Hands-on experience with AI/ML platforms and modern data architectures.
 </li>
 <li>
 Experience in regulated industries (e.g., Banking, Telecom, Healthcare) is a plus.
 </li>
 <li>
 Strong problem-solving, analytical thinking, and communication skills.
 </li>
 <li>
 Experience working in cross-functional teams and agile environments.
 
 
 </li>
 </ul>
 
 2&ndash; Technical Skills
 
 
 
 <ul>
 <li>
 Strong SQL and advanced data modeling techniques
 </li>
 <li>
 Apache Spark (PySpark, Spark SQL, Streaming)
 </li>
 <li>
 Python (pandas, PySpark, data processing libraries)
 </li>
 <li>
 Data pipeline orchestration (Apache Airflow)
 </li>
 <li>
 CI/CD for data pipelines (GitHub Actions / Azure DevOps)
 </li>
 <li>
 Lakehouse architectures (Delta Lake / Iceberg / Hudi)
 </li>
 <li>
 Streaming technologies (Kafka, Flink)
 </li>
 <li>
 Cloud platforms (AWS / Azure / GCP)
 </li>
 <li>
 Vector databases (Pinecone, Weaviate, pgvector, OpenSearch)
 </li>
 <li>
 RAG pipeline design and LLM data processing
 </li>
 <li>
 Infrastructure-as-Code (Terraform / Bicep / CDK)
 </li>
 <li>
 Containers (Docker, Kubernetes)
 </li>
 <li>
 Data quality &amp; governance tools
 </li>
 </ul>
</div>

Senior AI Data Engineer

ProfitSolv

<div>ProfitSolv is a SaaS business services provider for the legal and accounting industry. We are looking for an AI Data Engineer to join our growing team!
We are seeking a seasoned AI Data Engineer to support in building a centralized data platform on AWS to unify data across the entire portfolio and power the next generation of AI-driven experiences for our customers.
This is a greenfield building. We have a clearly defined three-phase architecture (Lakehouse standup database migration zero-ETL endgame) and executive sponsorship. We&rsquo;re looking for an engineer who is an equal parts data engineer and AI builder - someone who can write dbt models in the morning and design a RAG pipeline in the afternoon.
What we provide:
<ul><li>Opportunity to Invest in Your Future. We offer a 401K match.
</li><li>Paid Time Off. Enjoy paid time off and paid holidays.
</li><li>Great Coverage. Take advantage of health, dental, and vision HSA and FSA policies.
</li><li>A Great Team. Collaborate with smart, curious, hardworking individuals.
</li><li>Performance Compensation. Be rewarded for your hard work with performance-based merits.
</li><li>Remote Work. Want to work from home? No problem!</li></ul> 
 
As an AI Data Engineer, you will:
<ul><li>Build and maintain a medallion Lakehouse (Bronze/Silver/Gold) on S3 using Apache Iceberg, Glue Data Catalog, and dbt Cloud with the Athena adapter.</li>
<li>Configure and manage AWS DMS for ongoing CDC from ~1,000 SQL Server instances. Build ECS Fargate tasks for SaaS API ingestion. Orchestrate it all with Amazon MWAA (Airflow).</li>
Write dbt Cloud models for Bronze Silver<li> Gold transforms. Define business metrics in the dbt Semantic Layer so BI tools and AI agents can consume them.</li>
<li>Manage Redshift Serverless + Spectrum as the read engine for analysts and BI tools. Tune Iceberg table layouts, partitioning, and compaction for performance.</li>
<li>Implement Lake Formation tag-based governance for multi-product data isolation. Onboard new acquisitions to the platform in weeks, not months.</li>
<li>Build batch embedding pipelines for legal documents and client records. Manage vector storage in OpenSearch Serverless or pgvector on Aurora.</li>
<li>Design and ship RAG pipelines: chunking strategies, retrieval ranking, context window management - for legal domain use cases.</li>
<li>Build MCP servers that expose the dbt Semantic Layer and data platform APIs to AI agents (Claude, internal copilots, customer-facing features).</li>
<li>Ensure compliance, security, and governance through IAM roles, encryption policies, and metadata cataloging.</li>
<li>Other duties as assigned.</li>
</ul>
This position follows established policies and procedures to keep confidential information secure. 
 
A great fit for this position has:
<ul><li>5+ years of hands-on data engineering experience, with a strong focus on AWS services such as S3, Glue, Athena, and Redshift (or equivalent platforms).</li>
<li>Proven experience building and maintaining production-grade data models using dbt (Core or Cloud), including testing, macros, and documentation best practices.</li>
<li>Experience implementing Change Data Capture (CDC) patterns using tools such as AWS DMS, Debezium, or similar technologies from relational databases.</li>
<li>Demonstrated ability to design, build, and operate production Airflow DAGs (MWAA or self-hosted environments).</li>
<li>Hands-on experience developing at least one production-ready Retrieval-Augmented Generation (RAG) pipeline, including data chunking, embedding generation, vector storage, and retrieval mechanisms.</li>
<li>Strong proficiency in SQL (primary language) and Python (for data pipelines and AI workflows), with working knowledge of TypeScript for MCP server development.</li>
<li>Experience with infrastructure-as-code tools such as Terraform or equivalent solutions.</li>
<li>Comfortable working across the full technology stack in a high-autonomy environment, with the ability to make architectural decisions and drive solutions independently.</li>
</ul>
Additional Desirable Qualifications
<ul><li>Experience building MCP servers or similar AI tool-use integrations.</li>
<li>dbt Cloud Semantic Layer / MetricFlow experience.</li>
<li>Worked with Apache Iceberg, Delta Lake, or Hudi in production.</li>
SQL Server<li> Aurora PostgreSQL migration experience.</li>
<li>Worked in PE-backed, multi-product, or M&amp;A-heavy environments.</li>
<li>Legal tech, payments, or professional services domain knowledge.</li>
<li>Ability to sit for prolonged periods at a desk and work on a computer.
</li><li>Must be able to lift up to 15 pounds at times.
</li><li>Ability to handle stress
</li><li>Ability to meet work deadlines
</li></ul>Our commitment to you: At ProfitSolv, we are committed to being a diverse and inclusive workplace as an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law. We embrace a diverse group of backgrounds and experiences to connect with clients, solve problems, and innovate. 
 
Work location: Remote &ndash; U.S. only</div><div></div>

AI Data Engineer - Remote

Lockheed Martin

<div>JOB ID: 727225BR
Date posted: May. 06, 2026
 
Description:We are seeking a senior‑level AI Data Engineer to design, build, and maintain scalable data pipelines and warehouse solutions that fuel AI/ML initiatives across the organization. The ideal candidate will have a proven track record of delivering enterprise‑grade data platforms, strong expertise with SAP HANA and modern cloud data services, and recent experience in AI/ML workflows (e.g., data preparation for model training, feature stores, model‑ready data delivery). 
 
Key Responsibilities 
<ul><li>Design, build, and maintain secure data pipelines for classified AI/ML workloads</li><li>Design and implement ontology-driven data models to support knowledge representation and reasoning</li><li>Collaborate with data scientists, analysts, and mission stakeholders to operationalize AI models and knowledge graphs</li><li>Implement rigorous data validation, lineage, and governance controls in compliance with security requirements</li><li>Collaboration with data architects and data analysts to develop a solution across the entire data processing pipeline</li><li>Experience working with business stakeholders to develop and deploy data analytics solutions and/or custom software application solutions and leading teams to implement these solutions</li><li>Optimize performance and reliability of large-scale data systems under constrained environments</li><li>Mentor junior engineers and uphold secure coding and engineering best practices</li><li>Responsible for collecting, ingesting, processing, and storing large datasets from a wide variety of data sources and stakeholders</li><li>Able to perform impact analysis including lineage, upstream and downstream dependencies.
</li><li>Support after hours on-call operations on a rotation basis</li><li>US Citizenship</li></ul> 
#EBDT 
Basic Qualifications: 
<ul><li>Degree in Computer Science, Systems Engineering, or related field</li><li>10+ years of experience in data engineering, including 3+ years supporting AI/ML systems - traditional business intelligence and data warehousing concepts (ETL, data modeling, and reporting)
</li><li>5+ years of experience in data analysis, data/system architecture
</li><li>Experience in gathering requirements and understanding business processes</li><li>Strong proficiency in Python, NiFi, SQL</li><li>Experience with distributed data processing frameworks (e.g., Spark or similar)</li><li>Hands-on experience building ETL/ELT pipelines at scale</li><li>Familiarity with secure cloud environments (e.g., AWS GovCloud, Azure Government) or on-prem systems</li><li>Understanding of data security principles, including encryption, access control</li><li>Familiar with Safe Agile framework</li></ul>
Desired Skills: 
<ul><li>Experience designing and building ontologies, taxonomies, or knowledge graphs</li><li>Demonstrate ability to establish strong team relationships and deliver an exceptional customer experience</li><li>Excellent organizational, time management, analytical and problem-solving skills</li><li>Master&rsquo;s degree in a related technical field</li></ul>
Clearance Level:None 
Other Important Information You Should Know 
Expression of Interest:By applying to this job, you are expressing interest in this position and could be considered for other career opportunities where similar skills and requirements have been identified as a match. Should this match be identified you may be contacted for this and future openings. 
Ability to Work Remotely:Full-time Remote Telework: The employee selected for this position will work remotely full time at a location other than a Lockheed Martin designated office/job site. Employees may travel to a Lockheed Martin office for periodic meetings. 
Work Schedules:Lockheed Martin supports a variety of alternate work schedules that provide additional flexibility to our employees. Schedules range from standard 40 hours over a five day work week while others may be condensed. These condensed schedules provide employees with additional time away from the office and are in addition to our Paid Time off benefits. 
Schedule for this Position:4x10 hour day, 3 days off per week 
Pay Rate: The annual base salary range for this position in most major metropolitan areas in California, Massachusetts, and New York is $131,000 - $227,125. For states not referenced above, the salary range for this position will reflect the candidate&rsquo;s final work location. Please note that the salary information is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market and business considerations when extending an offer. 
Benefits offered: Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Education Assistance, Parental Leave, Paid time off, and Holidays. 
This position is incentive plan eligible. 
Pay Rate: The annual base salary range for this position in California, Massachusetts, and New York (excluding most major metropolitan areas), Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, Vermont, Washington or Washington DC is $113,900 - $200,905. For states not referenced above, the salary range for this position will reflect the candidate&rsquo;s final work location. Please note that the salary information is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market and business considerations when extending an offer. 
Benefits offered: Medical, Dental, Vision, Life Insurance, Short-Term Disability, Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Education Assistance, Parental Leave, Paid time off, and Holidays.
 
(Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year. 
This position is incentive plan eligible. 
Lockheed Martin is an equal opportunity employer. Qualified candidates will be considered without regard to legally protected characteristics. 
The application window will close in 90 days; applicants are encouraged to apply within 5 - 30 days of the requisition posting date in order to receive optimal consideration. 
At Lockheed Martin, we use our passion for purposeful innovation to help keep people safe and solve the world's most complex challenges. Our people are some of the greatest minds in the industry and truly make Lockheed Martin a great place to work.
 
 
With our employees as our priority, we provide diverse career opportunities designed to propel, develop, and boost agility. Our flexible schedules, competitive pay, and comprehensive benefits enable our employees to live a healthy, fulfilling life at and outside of work. We place an emphasis on empowering our employees by fostering an inclusive environment built upon integrity and corporate responsibility. 
 
If this sounds like a culture you connect with, you&rsquo;re invited to apply for this role. Or, if you are unsure whether your experience aligns with the requirements of this position, we encourage you to search on Lockheed Martin Jobs, and apply for roles that align with your qualifications. 
Experience Level:Experienced Professional 
Business Unit:ENTERPRISE BUSINESS SERVICES 
Relocation Available:No 
Career Area:Information Technology 
Type:Full-Time 
Shift:First</div>
 <div>At Lockheed Martin, we apply our passion for purposeful innovation to keep people safe and solve the world's most complex challenges.
Pioneering Defense Technology: From aerospace to outer space to cyber space, you can innovate mission solutions alongside the best minds in the business.
United By Culture: Excellence, integrity, and collaboration define us. We accelerate change and embrace one another&rsquo;s perspectives to win for our customers.
Real Impact, Real Growth: Grow your career and skills for life. Our wide array of opportunities and technology-driven learning programs enable your development and agility.
Your Health, Your Wealth, Your Life: Competitive pay, comprehensive benefits and flexible schedules designed so you thrive &mdash; at work and beyond.
Empowered to Be Your Best: Use your strengths to make a difference in the lives of one another, our customers, our communities, and our planet.
Here, the possibilities are endless because we offer:
<div></div><ul><div>Flexible Schedules, dependent on role</div>
<div></div><div>Levels: Student, Entry, Mid, Senior, Management</div>
<div></div><div>Locations: Nationwide &amp; OCONUS Positions</div></ul></div>

AI Data Engineer Stf

TechLabs London

<div>
 
 About TechLabs London:
 
 
 
 At TechLabs London, we build cutting-edge digital products that power smarter, more connected organizations. Our flagship solution is transforming housing management with modern cloud technology, intuitive design, and seamless integration. We are now embedding
 
 AI and intelligent automation
 
 across our platforms &mdash; and this role is at the center of that mission. We&rsquo;re looking for a driven and creative
 
 AI Engineer
 
 to lead this charge with us, to deliver production-ready AI features, predictive analytics, and computer vision tools that unlock real business value.
 
 
 
 Job Purpose:
 
 As an AI Engineer you&rsquo;ll be responsible for the development, deployment, and optimisation of advanced AI and ML capabilities within TechLabs London. You will collaborate with cross-functional teams to embed intelligent solutions into our existing product suite and shape the next generation of AI-powered features across our platforms. This role is perfect for someone who&rsquo;s passionate about building practical AI solutions and wants to see their work improve lives, save time, and power decisions. You&rsquo;ll help the business translate complex challenges into intelligent, scalable solutions.
 
 
 
 Responsibilities:
 
 
 
 <ul>
 <li>
 Design and build end-to-end AI pipelines across use cases such as predictive modelling, NLP, computer vision, and conversational AI.
 </li>
 <li>
 Design, develop and deploy conversational AI solutions using LLMs and agentic frameworks (e.g. LangChain, LangGraph), including RAG pipelines, intent classification, entity extraction, and multi-turn dialogue management.
 </li>
 <li>
 Apply computer vision to extract intelligence from images and videos for detection, classification, and automation.
 </li>
 <li>
 Develop time-series models that analyze behavior and generate predictive.
 </li>
 <li>
 Experiment with foundational models and fine-tune them for specific tasks relevant to our domain.
 </li>
 <li>
 Integrate models into production by working with our developers to deploy solutions into our products and beyond.
 </li>
 <li>
 Architect and build scalable, secure, and reusable ML APIs, workflows, and pipelines using modern MLOps practices and containerisation (Docker).
 </li>
 <li>
 Translate business problems into data-driven solutions, working closely with product, customer success, and engineering teams.
 </li>
 <li>
 Champion AI-first thinking across the company, supporting POCs, internal tooling, and customer-facing features.
 </li>
 <li>
 Collaborate with internal developers, product managers, domain experts, and external stakeholders to ensure solutions are feasible, maintainable, and impactful, as well as to define data strategies and model objectives.
 </li>
 <li>
 Conduct rigorous A/B testing and model evaluation.
 </li>
 <li>
 Support the integration of models into production systems using APIs and microservices.
 </li>
 <li>
 Stay abreast of advancements in AI/ML research and apply relevant breakthroughs to ongoing projects.
 
 
 
 </li>
 </ul>
 
 Skills and Experience:
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in information technology, Software Engineering, Computer Science, or related field.
 </li>
 <li>
 Proven experience building and deploying LLM-powered applications, including RAG pipelines and conversational agents.
 </li>
 <li>
 Hands-on experience with LangChain or similar agentic frameworks (LangGraph, AutoGen, or equivalent).
 </li>
 <li>
 Strong proficiency in Python and relevant ML libraries (e.g. pandas, scikit-learn, PyTorch, spaCy, Hugging Face, OpenCV etc.)
 </li>
 <li>
 Experience with cloud platforms &mdash; Azure is our primary platform (preferred); AWS or GCP also considered.
 </li>
 <li>
 Containerization and orchestration: Docker, Docker Compose.
 </li>
 <li>
 Solid understanding of data modelling, feature engineering, and data warehousing concepts.
 </li>
 <li>
 Strong knowledge of SQL and experience with relational and/or vector databases (e.g. pgvector, Pinecone, Weaviate).
 </li>
 <li>
 Proficiency in version control, CI/CD, and model deployment practices.
 </li>
 <li>
 Experience integrating AI models into web or mobile applications is highly valued.
 </li>
 <li>
 Experience with MLOps tools like MLflow, Airflow, DVC.
 </li>
 <li>
 Familiarity with MCP Server (Model Context Protocol) for building tool-augmented AI agents is a plus.
 
 
 
 </li>
 </ul>
 
 Mindset &amp; Soft Skills:
 
 
 
 <ul>
 <li>
 Strong problem-solving and analytical thinking &mdash; able to translate messy real-world data into measurable solutions.
 </li>
 <li>
 Pragmatic and delivery-oriented &mdash; capable of balancing technical innovation with business impact.
 </li>
 <li>
 Communicative and collaborative &mdash; comfortable working across teams and disciplines.
 </li>
 <li>
 Self-driven &mdash; able to manage time, priorities, and project ownership in a fast-moving team.
 </li>
 <li>
 Knowledge of data governance, bias mitigation, and explainable AI techniques.
 </li>
 </ul>
</div>

AI& Data Engineer (Hybrid)

Ｃollective

<div>About Collective:
Collective is on a mission to redefine the way businesses-of-one work. Our technology and team of trusted advisors help members achieve financial independence by taking care of everything from business incorporation to accounting, bookkeeping, tax services, and access to a thriving community, all in one integrated platform. We believe in empowering self-employed people to enjoy the same tax savings that big companies get, so they can focus on their passion, not paperwork.
Featured in Forbes, Business Insider, Yahoo, Bloomberg, Financial Times, TechCrunch, and more. We are backed by General Catalyst, Sound Ventures (Ashton Kutcher and Guy Oseary), QED Investors, Google&rsquo;s Gradient Ventures, Expa, and other investors who have financed iconic companies like YouTube, Substack, Twitch, Box, Hims, Instacart, and Lyft.
About the role:
Most teams have the data they need but can&rsquo;t access it. This role is here to fix that.
We're hiring a senior engineer to build an internal data agent: a system that lets anyone at the company act as their own data analyst. Individuals can ask questions in plain English and get back correct, decision-ready answers or dashboards.
This is not a traditional data role. You'll operate across AI systems, data infrastructure, and product engineering. You'll be responsible both for building a powerful system and for ensuring the data it relies on is clean, consistent, and reliable.
The problem space is wide open. We&rsquo;re looking for a strategic operator to shape the technical direction, the data model, and system adoption.
What you'll do:<ul><li>
Develop agent infrastructure<ul><li>
Build retrieval, planning, and execution systems on top of real data</li><li>
Support multi-step reasoning and analysis workflows</li><li>
Integrate into internal tools</li></ul></li><li>
 Build an end-to-end analytics system<ul><li>
Create a natural language interface over our entire data stack</li><li>
Enable anyone to run complex analyses without writing SQL</li></ul></li><li>
Own and improve the data layer<ul><li>
Collaborate with product engineering to design and evolve clean, consistent data models</li><li>
Identify inconsistencies in the core schema and resolve them at the source</li></ul></li><li>
Build and maintain data pipelines<ul><li>
Own the reliability of our data infrastructure end-to-end</li><li>
Debug and fix broken systems (e.g. dbt models, pipeline failures)</li><li>
Make it easy to integrate new data sources into the data warehouse</li></ul></li><li>
Make it trustworthy<ul><li>
Ensure outputs are correct, consistent, and explainable</li><li>
Build evaluation loops, monitoring, and guardrails</li><li>
Eliminate ambiguity in metrics, definitions, and sources of truth</li><li>
Handle permissions, sensitive data, and edge cases</li></ul></li><li>
Drive adoption<ul><li>
Work directly with teams across EPD, Member Operations, Legal, and more</li><li>
Drive alignment on data definitions and system usage</li><li>
Turn this into the default way people interact with data</li></ul></li></ul>
What you'll bring:<ul><li>
 You are a product engineer working in data<ul><li>
You treat data models as part of the product, not a separate layer</li><li>
You have strong opinions on schema design, naming, and consistency</li><li>
You're comfortable identifying issues in production data and fixing them at the root</li></ul></li><li>
You are an owner, not a participant<ul><li>
You take vague, high-stakes problems and turn them into real systems</li><li>
You care about outcomes, not just implementation</li></ul></li><li>
You have strong data experience<ul><li>
Experience with data modeling, warehouses, and pipelines</li></ul></li><li>
You're an exceptional builder<ul><li>
You can go from idea architecture production</li><li>
You've built with LLMs, RAG, or agents in production</li><li>
You've shipped systems that people actually rely on</li></ul></li><li>
You have influence<ul><li>
You communicate effectively with both technical and non-technical stakeholders</li><li>
You can drive alignment across teams with competing priorities</li><li>
You're comfortable challenging existing systems and pushing for better approaches</li></ul></li></ul>
What we offer:<ul><li>
Hybrid Work Model: Based in San Francisco with a balance of in-office and remote flexibility.</li><li>
Fresh Lunch: Provided on in-office days.</li><li>
Commuter Support: $150 monthly reimbursement for transit expenses.</li><li>
Health &amp; Wellness: $200 quarterly reimbursement to support your well-being.</li><li>
Time Off: Flexible PTO plus 14 company holidays.</li><li>
Comprehensive Coverage: 100% medical, dental, and vision for employees; 75% coverage for dependents.</li><li>
Parental Leave: 16 weeks fully paid.</li><li>
Retirement &amp; Ownership: 401k plan plus an equity package.</li><li>
Team Connection: Quarterly virtual events and an annual in-person summit.</li></ul>
Compensation Range: $200K - $270K</div>

AI Data Engineer (Agent Platform)

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