ml infrastructure engineer

Fission Labs

<div></div><div><div><div><div><div>Hyderabad</div><div>
7-10 Years</div></div><div><div>
Full Time</div></div><div><div><div><div> 
</div></div></div><div></div><div><div><div><div>
</div></div></div></div></div></div></div><div></div><div><div><h4 class="jobSectionHeader">Roles and Responsibilities</h4><div>
What are we looking for!
We are seeking an experienced Senior Infrastructure Mgmt. Engineer with expertise in Azure and AWS, coupled with a strong background in AI/ML deployments. The ideal candidate will have a proven track record in designing, implementing, and maintaining scalable and secure cloud infrastructure on Azure and AWS platforms. Experience with GCP is a plus. The primary responsibility will be to lead the DevOps initiatives and ensure compliance with industry standards and regulations.
Responsibilities
Linux Expertise:<ul><li>
Possess in-depth knowledge of Linux operating systems, including CentOS, Ubuntu, and Red Hat, with expertise in shell scripting, package management, and system administration.</li><li>
Configure and optimize Linux-based servers for performance, security, and resource utilization, including kernel tuning, file system management, and network configuration.</li></ul>
Cloud Expertise (AWS/Azure)<ul><li>
Demonstrate hands-on experience with a wide range of AWS and Azure services, including but not limited to EC2, S3, Lambda, RDS, Azure VMs, Azure Blob Storage, Azure Functions, etc.</li><li>
Architect cloud solutions leveraging best practices and services offered by AWS and Azure, optimizing for scalability, reliability, and cost-effectiveness.</li><li>
Implement and manage hybrid cloud environments, facilitating seamless integration and interoperability between AWS and Azure services.</li></ul>
Infrastructure as Code (IAC):<ul><li>
Develop and maintain Infrastructure as Code (IAC) templates using tools such as Terraform or AWS CloudFormation, defining infrastructure components as code for automated provisioning and configuration.</li><li>
Establish version control practices for IAC templates, ensuring traceability, auditability, and reproducibility of infrastructure changes.</li></ul>
AI/ML Infrastructure Mgmt:<ul><li>
Experience setting up cloud infrastructure stack, databases, service endpoints, GPU as well as CPU resource scaling, optimization etc.</li><li>
Should have worked AIOps/MLOP</li><li>
Should have worked on deploying AI/ML Apps using Docker and Kubernetes</li><li>
Should have worked on scaling, high availability and reliability tasks for AI application</li><li>
Should have worked on deploying and maintaining GPU clusters for AI/ML training and inferenc</li></ul></div></div></div><div><div><h4 class="jobSectionHeader">
Qualifications Required</h4><div><h5 class="jobSectionHeader">
Qualifications</h5><ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering, or related field</li></ul>
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Skills and Experience Required</h4><div>
Responsibilities<ul><li>
7+ years of experience in Infrastructure Mgmt. roles, with a focus on cloud platforms (Azure and AWS Preferred).</li><li>
Hands-on experience with operations (DevSecOps) principles and best practices.</li><li>
Proficiency in scripting languages such as Python, PowerShell, or Bash.</li><li>
Excellent communication and collaboration skills.</li><li>
Certifications such as AWS Solution Architect Associate, AWS Cloud Practitioner, Azure DevOps Engineer Expert, Azure Administrator</li><li>
Certified Kubernetes Administrator or relevant industry certifications are a plus.</li></ul>
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Why you'll love working with us:</h4><div><h5 class="jobSectionHeader">
You Would Enjoy</h5><ul><li>
Opportunity to work on impactful technical challenges with global reach.</li><li>
Vast opportunities for self-development, including online university access and knowledge sharing opportunities.</li><li>
Sponsored Tech Talks &amp; Hackathons to foster innovation and learning.</li><li>
Generous benefits packages including health insurance, retirement benefits, flexible work hours, and more.</li><li>
Supportive work environment with forums to explore passions beyond work.</li></ul>
This role presents a unique opportunity to contribute to the future of impactful business solutions while advancing your career in a collaborative and innovative environment.</div></div></div></div>

Senior Devops Engineer (With AI/ML)

Siemens

<div>
 
 About Brightly
 
 
 
 Brightly is a global leader in intelligent, cloud‑based asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planning&mdash;backed by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.
 
 
 
 The Opportunity
 
 
 
 We&rsquo;re looking for a Senior Machine Learning Engineer to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
 </li>
 <li>
 Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.
 </li>
 <li>
 Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
 </li>
 <li>
 Scale training &amp; inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
 </li>
 <li>
 MLOps &amp; governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
 </li>
 <li>
 Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability.
 </li>
 <li>
 Mentor &amp; lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
 </li>
 <li>
 Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.
 
 
 </li>
 </ul>
 (Consistent with ML engineer responsibilities to analyze data before model development.)
 
 
 <ul>
 <li>
 Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Required experience
 
 
 
 <ul>
 <li>
 5&ndash;7 years total software/ML engineering experience, with 3+ years building and operating ML systems in production.
 </li>
 <li>
 1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.
 </li>
 <li>
 AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)
 </li>
 <li>
 Modeling &amp; frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
 </li>
 <li>
 MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
 </li>
 <li>
 Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.
 
 
 </li>
 </ul>
 
 Nice to have
 
 
 
 <ul>
 <li>
 Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
 </li>
 <li>
 Exposure to sustainability/asset/intelligent operations domains.
 </li>
 <li>
 Familiarity with security &amp; compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)
 
 
 </li>
 </ul>
 How you&rsquo;ll work
 
 
 <ul>
 <li>
 Pragmatic and product‑oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
 </li>
 <li>
 Engineering excellence: You write production‑quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
 </li>
 <li>
 Collaborative leadership: You mentor peers and influence architecture across teams. (Industry‑standard senior expectations.)
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in CS/EE/Math or related field (Master&rsquo;s preferred) or equivalent practical experience. (Typical for senior ML roles.)
 
 
 </li>
 </ul>
 Join a mission‑driven team building technology that keeps communities running&mdash;safer, greener, and more resilient&mdash;at global scale. You&rsquo;ll pair startup‑speed product work with the reach and rigor of Siemens.
 
 
 
 The Brightly culture
 
 
 
 We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.
</div>

Senior Machine Learning Engineer

EverFi

<div>Everfi is a leading education technology company founded in 2008 that delivers digital learning solutions focused on real-world skills. The company provides scalable education in areas such as financial literacy, health and wellness, and workplace readiness.
We are seeking a Cloud Architecture Engineer, Senior Principal to support the reliability, scalability, security, and strategic evolution of EVERFI's PaaS systems, while acting as a force multiplier for the broader engineering organization. The Cloud Architecture Engineer, Senior Principal is expected to operate with a high degree of autonomy, make consequential architectural decisions, and represent cloud infrastructure to senior leadership as needed. 
Cloud Architecture Engineer, Senior Principal Compensation and Benefits
$170,000-$185,000 annual base salary depending on experience and education. Everfi may pay more or less based on employee qualifications, market value, Company finances, and other operational considerations.
This role is eligible to participate in the Corporate Bonus Plan
100% Remote position
Health, Dental, and Vision insurance
401(K) with matching contribution
Generous Paid Time Off (PTO) 
Cloud Architecture Engineer, Senior Principal Responsibilities
Strategic Architecture &amp; Technical Vision<ul><li>
Shape and execute the multi-year cloud architecture strategy for EVERFI's platform, aligning infrastructure direction with business growth, product roadmap, and organizational scalability goals</li></ul><ul><li>
Lead enterprise-wide architecture reviews and establish cloud governance standards, guardrails, and best practices adopted across all engineering teams</li></ul><ul><li>
Identify and evaluate emerging technologies (e.g., AI/ML infrastructure, FinOps, platform engineering tooling) and make forward-looking recommendations to VP and CTO leadership</li></ul><ul><li>
Serve as a technical decision-maker and escalation point for complex, cross-functional cloud architecture challenges</li></ul><ul><li>
Drive the evolution of EVERFI's platform from reactive infrastructure management to proactive, resilience-first platform engineering</li></ul>
Platform Engineering &amp; Infrastructure<ul><li>
Design, build, maintain, and monitor internal PaaS systems in the cloud at enterprise scale &mdash; with a focus on high availability, fault tolerance, and cost efficiency</li></ul><ul><li>
Lead the implementation of geographically redundant, multi-region, and highly available architectures capable of supporting EVERFI's growth trajectory</li></ul><ul><li>
Architect and oversee the full lifecycle of cloud deployment tooling, ensuring CI/CD systems are continuously optimized for speed, safety, and developer experience</li></ul><ul><li>
Design reliable, interoperable components and utility libraries that underpin EVERFI's applications across engineering teams</li></ul><ul><li>
Architect and own a comprehensive metrics, monitoring, and observability system enabling full-stack visibility, proactive incident response, and SLA enforcement</li></ul>
Security, Compliance &amp; Risk<ul><li>
Ensure all cloud environments are designed and administered in accordance with company security guidelines, SOC 2 compliance requirements, FERPA, and applicable data privacy regulations</li></ul><ul><li>
Architect zero-trust networking, identity and access management (IAM), and data encryption frameworks across EVERFI's cloud environments</li></ul><ul><li>
Partner with Security and Legal teams to lead cloud risk assessments and drive remediation of architectural vulnerabilities at the platform level</li></ul>
Cross-Functional Leadership &amp; Influence<ul><li>
Act as a senior technical advisor to Engineering, Product, and Executive leadership on all cloud infrastructure decisions &mdash; translating complex technical tradeoffs into clear business impact</li></ul><ul><li>
Champion engineering culture: establish and promote best practices for reliability, performance, and operational excellence across all teams consuming cloud infrastructure</li></ul><ul><li>
Build training programs, playbooks, and knowledge resources that upskill engineering teams on cloud operations, architecture patterns, and performance optimization</li></ul><ul><li>
Represent the Cloud Architecture function in cross-functional planning, capacity modeling, and strategic workforce design discussions</li></ul>
Mentorship &amp; Talent Development<ul><li>
Provide mentorship, technical guidance, and career development support to Principal, Staff, and Senior Engineers on the Cloud Architecture team and adjacent teams</li></ul><ul><li>
Participate in hiring, technical interviews, and calibration discussions as needed</li></ul><ul><li>
Participate in an on-call rotation to provide emergency incident support as needed, modeling expected standards of operational excellence for the team</li></ul> 
Cloud Architecture Engineer, Senior Principal Qualifications<ul><li>
Bachelor's Degree in Computer Science, Engineering, or equivalent combination of education and experience required; Master's degree strongly preferred</li></ul><ul><li>
10+ years of progressive professional experience in cloud infrastructure, platform engineering, or systems architecture</li></ul><ul><li>
Minimum 3&ndash;5 years of experience operating at the Principal or Staff Engineer level, with demonstrated ownership of enterprise-wide architectural decisions</li></ul><ul><li>
Proven track record of leading large-scale cloud transformations, platform modernizations, or migrations in high-growth SaaS environments</li></ul><ul><li>
Experience in EdTech, FinTech, or compliance-heavy industries strongly preferred</li></ul>
Certifications (Preferred)<ul><li>
AWS Solutions Architect &ndash; Professional (or equivalent Azure/GCP professional-level certification)</li></ul><ul><li>
AWS Security Specialty or Certified Cloud Security Professional (CCSP)</li></ul><ul><li>
Kubernetes Administrator (CKA) or equivalent</li></ul>
Technical Skills<ul><li>
Cloud Platforms: Deep expertise in AWS (required); hands-on experience with Azure and/or GCP preferred</li></ul><ul><li>
Infrastructure as Code: Terraform, AWS CloudFormation, Pulumi, or CDK at enterprise scale</li></ul><ul><li>
Containerization &amp; Orchestration: Docker, Kubernetes (EKS/AKS/GKE), Kafka</li></ul><ul><li>
CI/CD &amp; DevOps: Advanced pipeline design with Jenkins, GitHub Actions, ArgoCD, or equivalent</li></ul><ul><li>
Networking &amp; Security: TCP/IP, DNS, SMTP, HTTP, VPCs, zero-trust architecture, WAF, distributed networks</li></ul><ul><li>
Programming &amp; Scripting: Python, Go, Ruby on Rails, or Bash (proficiency in at least two)</li></ul><ul><li>
Data &amp; Storage: SQL and NoSQL (Postgres and equivalents); familiarity with data lake and lakehouse architectures</li></ul><ul><li>
Observability: Datadog, New Relic, Prometheus/Grafana, or equivalent full-stack observability platforms</li></ul><ul><li>
AI/ML Infrastructure: Familiarity with cloud-native AI/ML infrastructure (SageMaker, Vertex AI, or equivalent) preferred</li></ul><ul><li>
Open-Source Ecosystem: Extensive experience contributing to and consuming open-source tooling in production environments</li></ul><ul><li>
Web Services &amp; APIs: Expert-level knowledge of REST, RPC, SOA, and API design principles</li></ul></div>

Cloud Architecture Engineer, Senior Principal

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

Enigma Search

<div>
 
 
 Role Summary
 
 
 
 We are seeking a Machine Learning Engineer who has experience working with small
 
 
 molecule drug discovery datasets and can hit the ground running to deliver high-impact,
 
 
 production-grade solutions to advance our programs. The ideal candidate will build and
 
 
 scale data and ML infrastructure across early research, lead optimization, and development
 
 
 phases of drug discovery pipeline. You will enable data-driven science while ensuring
 
 
 robust engineering practices and FAIR data principles.
 
 
 
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Support management of biobank scale datasets in Polaris, Maze&rsquo;s internal platform
 </li>
 </ul>
 
 supporting Compass, by building scalable data ingestion, cleaning, processing, and
 
 
 validation pipelines.
 
 <ul>
 <li>
 Work with scientific compute teams to design and deploy machine learning models to
 </li>
 </ul>
 
 support workflows in research and small molecule drug discovery (compound property
 
 
 prediction, assay data prediction, data analysis).
 
 <ul>
 <li>
 Lead the evaluation and integration of Large Language Models (LLMs) to automate data
 </li>
 </ul>
 
 ingestion workflows, enhance intelligent querying, and support user-facing variant
 
 
 association and scientific visualization platforms.
 
 <ul>
 <li>
 Design and operate scalable ML and data platforms leveraging Terraform (IaC) and Git-
 </li>
 </ul>
 
 based CI/CD pipelines, incorporating workflow orchestration, automated model
 
 
 lifecycle management, and production-grade monitoring and reliability.
 
 <ul>
 <li>
 Collaborate with development organization to evaluate and deploy ML tools that
 </li>
 </ul>
 
 support workflows across Regulatory, Clinical Operations, and Medical Affairs.
 
 <ul>
 <li>
 Collaborate cross-functionally translate scientific requirements into production-grade
 </li>
 </ul>
 
 Systems.
 
 
 
 
 
 
 Required Qualifications
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Computer Science, Machine Learning, Bioinformatics, Data
 </li>
 </ul>
 
 Engineering, or a related field.
 
 <ul>
 <li>
 3+ years of industry experience building production-grade data and ML pipelines,
 </li>
 </ul>
 
 preferably in life sciences supporting drug discovery.
 
 <ul>
 <li>
 Hands-on experience deploying AI/ML models in drug discovery applications (e.g.,
 </li>
 </ul>
 
 computational biology/chemistry workflows).
 
 <ul>
 <li>
 Experience with FAIR data principles and strong programming skills in Python and SQL
 </li>
 </ul>
 
 (R is a plus).
 
 <ul>
 <li>
 Proven experience in deploying and maintaining ML systems, including CI/CD,
 </li>
 </ul>
 
 workflow orchestration, and monitoring.
 
 <ul>
 <li>
 Experience with workflow orchestration tools (e.g., Airflow, Prefect).
 </li>
 <li>
 Experience with containerization and cloud infrastructure (Docker, Kubernetes, AWS or
 </li>
 </ul>
 
 similar).
 
 
 
</div>

Machine Learning Engineer - Drug Discovery

SIDRAM TECHNOLOGIES

<div>
 
 Role: AI/ML Engineer - Python &amp; AWS
 
 
 
 
 Location- Reston, VA(In Person Interview at Herndon VA )
 
 
 
 
 Duration: Fulltime/Contract
 
 
 
 
 Onsite: 5 Days Onsite
 
 
 
 
 Job Description
 
 
 
 <ul>
 <li>
 Core Responsibilities (AI/ML, Python, AWS, GenAI)
 </li>
 <li>
 Design and implement end-to-end AI/ML and Generative AI solutions using Python, including model training, evaluation, optimization, and deployment.
 </li>
 <li>
 Build and maintain cloud native applications on AWS using services such as Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
 </li>
 <li>
 Develop high performance Python microservices (Fast API/Flask) enabling scalable data pipelines, model inference, and real time analytics.
 </li>
 <li>
 Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM powered automation (chatbots, summarization, semantic search, anomaly detection).
 </li>
 <li>
 Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure as code (Terraform/CloudFormation) for reliable, automated deployments.
 </li>
 <li>
 Build robust MLOps workflows, including model versioning, containerized training/inference, automated retraining, monitoring, and performance tuning.
 
 
 </li>
 </ul>
 
 Rachael
 
 
 
 
 IT Services | Development | Staffing
 
 
 
 
 URL: http:/www.sidramtech.com |
 
 
 
 
 Email: rachael@sidramtech.com
 
 
 
 
 Direct: 4705239688
 
 
 
 
 Led by 25+ Years of Industry Experience
 
</div>

AI/ML Engineer - Python &amp

<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
 
 
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Agentic AI Data Engineer

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