mlops engineer

MANTECH

<div><p>MANTECH seeks a motivated, career, and customer-oriented <b>AI Engineer</b> for a new initiative. This effort supports the rapid design, deployment, operation, and sustainment of enterprise-scale AI, data, and mission platform capabilities across a cloud, edge, and classified operational environment.</p><p>
The <b>AI Engineer</b> supports the development and operationalization of advanced AI and agentic AI systems in production mission environments. You will accelerate operational AI adoption through a modern Hub-and-Spoke architecture, supporting mission applications and enterprise AI governance.</p><p></p><p><b>
Responsibilities include but are not limited to:</b></p><ul><li>
Design, develop, and operationalize full-lifecycle AI/ML operations (MLOps/LLMOps).</li><li>
Implement and deploy Retrieval-Augmented Generation (RAG) architectures in production.</li><li>
Build and maintain scalable AI orchestration frameworks and production-ready AI systems.</li><li>
Manage and optimize Kubernetes-based AI deployments, focusing on GPU inference optimization.</li><li>
Conduct core Python development for AI systems and integrate OpenAI-compatible APIs into mission solutions.</li><li>
Operationalize AI agents using advanced frameworks like LangGraph or Semantic Kernel.</li></ul><p></p><p><b>
Minimum Qualifications:</b></p><ul><li>
Must possess 7 or more years of experience in AI/ML production engineering, development, or related roles. Relevant additional experience will be considered in lieu of degree.</li><li>
Proven proficiency in Python development and experience with AI/ML libraries like TensorFlow or PyTorch.</li><li>
In-depth knowledge of MLOps/LLMOps principles and full machine learning lifecycle management.</li><li>
Experience with Kubernetes-based AI deployments and GPU optimization techniques.</li><li>
Proven experience designing, building, securing or monitoring scalable solutions within the Microsoft Azure ecosystem (e.g., Azure OpenAI, Azure Synapse, Databricks, or Azure Security Center).</li></ul><p></p><p><b>
Preferred Qualifications:</b></p><ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering or a related field.</li><li>
Experience with LangGraph, Semantic Kernel, and agentic AI systems.</li><li>
Familiarity with Ray, NVIDIA GPU ecosystems, or Vector databases.</li><li>
Advanced degree (Master's or PhD) in a technical field.</li></ul><p></p><p><b>
Clearance Requirements:</b></p><ul><li>
TS/SCI with the ability to obtain and maintain a polygraph</li></ul><p></p><p><b>
Physical Requirements:</b></p><ul><li>
The person in this position must be able to remain in a stationary position 50% of the time.</li><li>
Frequently communicates with co-workers, management, and customers, which may involve delivering presentations.</li><li>
Constantly operates a computer and other office productivity machinery.</li></ul></div><p></p>

AI Engineer

FTI Defense

<div>Overview:
<div>
FTI Defense is seeking a hands-on AI/ML Engineer to design, build, and deploy advanced machine learning solutions supporting defense and national security missions. This role focuses on execution in oversight, ideal for an engineer who thrives in the code, enjoys building end-to-end pipelines, and takes pride in seeing their work directly impact operational systems.</div><div></div><div><br>
FTI Defense delivers mission-focused solutions to the Department of Defense/Depratment of War (DoD/DoW) and Intelligence Community (IC) through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.</div>
Responsibilities:
<ul><li>Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.</li><li>
Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.</li><li>
Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).</li><li>
Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).</li><li>
Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.</li><li>
Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).</li><li>
Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.</li><li>
Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.</li><li>
Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.</li><li>
Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.</li></ul>
Education/Qualifications:
<div><b>
Minimum Requirements:</b></div><ul><li><b>
Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed.</b></li><li>
6-10+ years of professional experience developing and deploying AI/ML solutions in production environments.</li><li>
Minimum of 3 years' professional experience within the <b>Department of Defense/Department of War (DoD/DoW)</b> AI assurance, security, and deployment environments.</li><li>
Strong Python development skills with hands-on experience building AI/ML solutions.</li><li>
Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.</li><li>
Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent.</li><li>
Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).</li><li>
Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.</li><li>
Professional experience integrating AI capabilities into production systems or mission applications.</li></ul><div>
 <b>Preferred Qualifications:</b></div><ul><li>
Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning.</li><li>
Understanding of prompt engineering, retrieval quality, and grounding methods.</li><li>
Exposure to GPU-based or edge inference environments.</li><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, Data Science, or a related technical field.</li><li>
Active Secret clearance preferred; ability to obtain one is <b>required</b>.</li></ul><div></div><div><br>
#LI-KM1</div><div>
#LI-Remote</div></div>

AI/ML Engineer

The Hartford

<div><h2 class="jobSectionHeader"><b>Job Details</b></h2>
<div><div><div>Location:</div>
<div>Chicago, IL</div>
</div><div><div>Category:</div><div>
Data &amp; Analytics</div>
</div><div><div>Employment Type:</div>
<div>Full time
</div></div><div><div>Job Ref:</div><div>
R2625795-172</div>
</div></div><p></p><div><div>Data Engineer - GE08AE<p>
We&rsquo;re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals &ndash; and to help others accomplish theirs, too. Join our team as we help shape the future.<br>
</p><p></p><div>The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team.<br>
The Hartford is developing industry‑leading AI and machine learning capabilities to improve the various facets of the Global Specialty underwriting experience. On the Global Specialty Applied AI team, we utilize the latest AI products and frameworks to accelerate the processes that our partners touch day to day and advance the speed and intelligence with which we make our decisions. As a Machine Learning AI Engineer, you will play a key role in contributing to the designing, building, and operationalizing production‑grade AI solutions&mdash;partnering closely with product, engineering, and platform leaders to deliver measurable impact.<br>
<br>
Our core values<br>
<ul><li>We build AI solutions, not models. We are thoughtful in supporting the end-to-end business problem, with an eye to systems design.</li><li>We are trusted and transparent. We collaborate tightly with our partners and are mindful of their capacity to absorb change.</li><li>We provide assets that are safe to buy. Our products are delivered with a full monitoring solution to ensure our products continue to deliver as expected.</li><li>We will earn the right to influence. With humble confidence, we listen carefully to learn from our customers and become partners in problem solving.</li><li>We are practical and evolutional. We first deliver a minimally viable product and over time expand its sophistication based on feedback.</li></ul><br>
Responsibilities<br>
<ul><li>Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.</li><li>Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.</li><li>Accountable for deployment design, development and maintenance of both traditional ML and AI models.</li><li>Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams</li><li>Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.</li><li>Adopt and promote MLOps best practices to the Data Science community.</li></ul><br>
Minimum Requirements<br>
<ul><li>Must be authorized to work in the U.S. now and in the future.</li><li>1+ years of equivalent experience in a research or DevOps function.</li><li>Development experience developing solutions within AWS, GCP or both.</li><li>Exposure to developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.</li><li>Familiarity with building and deploying API services within the Cloud.</li><li>Familiarity building CICD pipelines using Jenkins or equivalent</li><li>Exposure with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents</li><li>Experience in Unix, git, and strong object oriented development experience using Python</li><li>Exposure to with workflow automation platforms (Apache Airflow, Autosys, similar)</li><li>Basic understanding of Data Science model development life cycle</li></ul></div><ul><li>Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM&rsquo;s into automated processes</li></ul><p>
This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).</p><p>
Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.</p><p><b>
Compensation</b></p><p>
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford&rsquo;s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:</p>
$100,960 - $151,440<p>
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age</p></div></div></div><p></p>

AI Machine Learning Engineer

Integrant

<div><p>We Are Hiring!</p><p>
Integrant is looking for game changers to join our team as &quot;Data Scientist - AI &amp; Machine Learning&quot; with below roles and responsibilities:</p><p></p><ul><li>
Use mathematics, statistics, machine learning, and artificial intelligence techniques to extract knowledge and insights from structured, semi-structured, and unstructured data.
</li><li>Design, develop, evaluate, and deploy predictive and prescriptive machine learning models.
</li><li>Conduct open research and experimentation to develop innovative solutions for complex client challenges.
</li><li>Engage with clients and stakeholders to understand business needs and translate them into AI and Data Science solutions.
</li><li>Design and implement end-to-end Machine Learning and Generative AI solutions.
</li><li>Build and optimize Retrieval-Augmented Generation (RAG) systems and intelligent agent-based applications.
</li><li>Develop scalable model deployment and monitoring solutions using MLOps best practices.
</li><li>Monitor model performance, detect concept drift, and continuously improve deployed systems.
</li><li>Collaborate with software engineering teams to productionize AI applications and ensure reliability, scalability, and maintainability.
</li><li>Mentor and coach junior Data Scientists and Machine Learning Engineers.
</li><li>Lead technical discussions, knowledge transfer sessions, and client-facing AI engagements.
</li><li>Stay current with emerging AI, Machine Learning, MLOps, and Generative AI technologies and frameworks.</li></ul><br>
<b>
Requirements</b><p><b><br>
Education &amp; Experience</b></p><ul><li><ul><li>
10+ years of professional experience, including 7+ years in Data Science, Machine Learning &amp; MLOps</li><li>
MSc in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.</li><li>
PhD is preferred, in related field.
</li><li>Experience mentoring, coaching, or leading technical team members.</li></ul></li></ul><p><b>
Data Science &amp; Machine Learning</b></p><ul><li><ul><li>
Strong foundation in Machine Learning techniques including Classification, Regression, Clustering, Association Rule Mining, Feature Engineering, and Model Evaluation.</li><li>
Experience with Deep Learning concepts and frameworks.</li><li>
Extensive hands-on experience with Python and the Data Science ecosystem.</li><li>
Experience with one or more ML frameworks such as Scikit-Learn, TensorFlow, Keras, or PyTorch.</li><li>
Experience conducting research, experimentation, and hypothesis-driven analysis.</li></ul></li></ul><p><b>
MLOps &amp; Production AI</b></p><ul><li><ul><li>
Experience deploying and managing Machine Learning models in production environments.</li><li>
Experience monitoring model performance, detecting concept drift, and driving continuous improvements.</li><li>
Hands-on experience with MLOps practices, CI/CD pipelines, model versioning, experiment tracking, monitoring, and observability.</li><li>
Experience deploying AI/ML solutions on cloud platforms such as Azure, AWS, GCP, or Databricks.</li><li>
Experience with ML platforms and services including Azure ML, AWS SageMaker, or Google Vertex AI.</li><li>
Familiarity with deployment and serving tools such as MLflow, FastAPI, and Streamlit.</li></ul></li></ul><p><b>
Generative AI &amp; Agentic AI</b></p><ul><li><ul><li>
Hands-on experience building Retrieval-Augmented Generation (RAG) solutions and semantic search applications.</li><li>
Experience working with Vector Databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search.</li><li>
Experience using LLM orchestration frameworks such as LangChain, LangGraph, or similar technologies.</li><li>
Experience working with Agentic AI frameworks such as LlamaIndex, CrewAI, AutoGen, or equivalent.</li><li>
Experience implementing MCP (Model Context Protocol), tool calling, and function-calling workflows.</li><li>
Strong understanding of prompt engineering techniques and LLM optimization.</li><li>
Experience evaluating LLM applications using frameworks such as LangSmith, RAGAS, or similar tools.</li><li>
Experience with Embeddings, Vector Retrieval, Semantic Search, Fine-Tuning, and LoRA techniques.</li></ul></li></ul><p>
Nice to Have:</p><p><b>
Advanced AI &amp; Data Science</b></p><ul><li>
Reinforcement Learning (RL)</li><li>
Optimization techniques, including single-objective and multi-objective optimization</li><li>
Stochastic Local Search methods</li><li>
Knowledge Graphs and Graph Machine Learning.</li></ul><p><b>
Cloud &amp; Data Engineering</b></p><ul><li>
Experience building large-scale data pipelines on Azure, AWS, or GCP.</li><li>
Experience with Databricks and Apache Spark.</li><li>
Experience with distributed data processing architectures.</li></ul><p><b>
Leadership &amp; Consulting</b></p><ul><li>
Experience leading AI initiatives and technical strategy.</li><li>
Experience working directly with international clients and stakeholders.</li><li>
Experience defining AI architecture, standards, and best practices across teams.</li></ul><br>
<b>
Benefits</b><br>
<ul>
<li>Salary paid in USD</li>
<li>Six-month career advancing opportunities</li>
<li>Employee parking space</li>
<li>Supportive and friendly work environment</li>
<li>Premium medical insurance [employee +family]</li>
<li>English language development courses</li>
<li>Interest-free loans paid over 2.5 years</li>
<li>Technical development courses</li>
<li>Planned overtime program (POP)</li>
<li>Employment referral program</li>
<li>Premium location in Maadi &amp; Nasr City</li>
<li>Social insurance</li>
<li>Opportunity to travel and work onsite with U.S. customers</li>
<li>In-house Technical and English training programs</li>
<li>Dedicated learning time (check out our 4Plus1 Program)</li>
<li>Flexible work schedules</li>
<li>Perks: events, sponsored lunch, game area, rooftop hangout + more!</li></ul></div>

Lead Data Scientist - AI & Machine Learning

Eli Lilly

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.
<br><br>
<b>Locations:</b> San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN
<br><br>
Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.
<br>Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.
<br><br>
Position Summary
<br><br>
We are seeking an Engineer - MLOps & Scientific Platforms - Data Foundry to operationalize Data Foundry’s scientific tools and analytical methods into actionable-prototypes. You will build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails that make our scientific discovery methods and tools reliable, scalable, and consumable, both by discovery scientists and by the Frontier AI group’s autonomous agents.
<br>This role sits at the interface between Methods4Insight (which develops analytical methods) and Architecture4Insight(which provides the agile data infrastructure). Your job is to ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs with the response-time guarantees and error handling that both human users and AI agents require.
<br><br>
Responsibilities
<br><br>
MLOps & Model Lifecycle Management
<br><br>
<ul>
<li>Build and maintain end-to-end ML deployment pipelines: experiment tracking, model versioning (MLflow, Weights & Biases), containerized model serving, and automated retraining triggers.</li>
<li>Develop model registry infrastructure and feature engineering pipelines that enable computational scientists to access models.</li>
<li>Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems (LLMOps) to ensure system reliability and performance at scale.</li>
<li>Build dashboards and metrics tracking for pipeline execution, API latency, token usage, model prediction quality, and system health</li>
<li>Establish structured logging and tracing infrastructure for debugging and performance optimization across scientific data systems</li>
</ul>
Scientific Tool Agile Deployment
<br><br>
<ul>
<li>Deploy predictive and analytical methods from Methods4Insight (e.g. cheminformatics, structural biology, bioinformatics, reaction informatics) with versioning, structured error handling, and response-time guarantees that enable insight generation in agile manner. Productionize when and where needed in partnerships with Tech@Lilly.</li>
<li>Build serving infrastructure supporting both synchronous (interactive scientist queries) and asynchronous (batch and agent-invoked) workloads in partnership with Tech@Lilly and Frontier AI.</li>
<li>Define and implement API contracts, documentation standards, and testing frameworks that ensure scientific tools are analysis ready, robust and consumable by external teams including Frontier AI.</li>
</ul>
Platform Engineering & Integration
<br><br>
<ul>
<li>Build and operate cloud-native model serving infrastructure (AWS, Azure, or GCP) using containers, Kubernetes, and infrastructure-as-code.</li>
<li>Develop CI/CD pipelines for ML models: automated validation, A/B testing, canary deployments, and rollback procedures.</li>
<li>Integrate model serving with Data Foundry’s data pipelines, ensuring models have access to properly formatted, versioned training and inference data.</li>
</ul>
Frontier AI Interface & Collaboration
<br><br>
<ul>
<li>Partner with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces (REST APIs, MCP-compatible endpoints) that agents can invoke programmatically.</li>
<li>Collaborate on API performance requirements: latency targets, throughput guarantees, and graceful degradation under load.</li>
<li>Work with Methods4Insight scientists to ensure deployed models include appropriate uncertainty quantification and confidence metrics.</li>
</ul>
Basic Requirements
<br><br>
<ul>
<li>B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field.</li>
<li>3+ years of experience in MLOps, ML engineering, or scientific platform development</li>
<li>Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.</li>
</ul>
Preferred Qualifications
<br><br>
<ul>
<li>Pharmaceutical or biotech research industry experience.</li>
<li>Strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and ML lifecycle tools (MLflow, W&B, Kubeflow, or similar).</li>
<li>Proven track record building and deploying production model serving infrastructure — containerized endpoints, RESTful/gRPC APIs, and operational monitoring</li>
<li>Working knowledge of cloud platforms (AWS, Azure, or GCP), Kubernetes, and CI/CD automation.</li>
<li>Strong communication skills with ability to collaborate across computational scientists, software engineers, and partner teams.</li>
<li>Experience operationalizing scientific or computational models (cheminformatics, bioinformatics, structural biology, QSAR, molecular simulations, PK/PD, systems biology, or ODE-based models).</li>
<li>Hands-on experience with model monitoring, drift detection, and automated retraining systems.</li>
<li>Familiarity with API gateway patterns, event-driven architectures, and service mesh technologies.</li>
<li>Experience with feature stores, data versioning (DVC), or experiment tracking at scale.</li>
<li>Exposure to AI agent frameworks (MCP, LangChain) or building APIs that AI systems invoke programmatically.</li>
<li>Experience with C, C++, CUDA, or GPU-accelerated computing for optimizing model training/inference performance; familiarity with containerizing HPC workloads (Singularity/Apptainer).</li>
</ul>
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
<br><br>
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
<br><br>
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women’s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
<br><br>
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
<br>$66,000 - $165,000
<br><br>
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
<br><br>
#WeAreLilly

Engineer - MLOps & Scientific Platforms - Data Foundry

Deloitte

<div>Our Deloitte AI &amp; Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.<br>
<br>
<b>Work You'll Do</b><br>
<br>
As a Data Management Engineer III on the project, you will:<br>
<ul><li>
Design, build, and deploy production-grade AI/ML systems across NLP, Retrieval-Augmented Generation (RAG), forecasting, and advanced analytics use cases.
</li><li>Fine-tune and operationalize large language models (LLMs) and RAG pipelines, including embedding strategy, vector store design, retrieval evaluation, and prompt/grounding patterns that meet the accuracy, traceability, and data-handling standards required in regulated health &amp; human services environments.
</li><li>Establish and maintain end-to-end MLOps on Azure (e.g., Azure ML, pipelines, model registry, CI/CD, monitoring, and drift detection), ensuring models are reproducible, secure, observable, and reliably maintained in production rather than one-off prototypes.
</li><li>Develop SQL data models and analytics solutions that integrate and structure data from child welfare and Medicaid program systems, translating fragmented agency data into trustworthy inputs for AI/ML systems and operational reporting that improve efficiency and surface actionable insight.
</li><li>Partner with cross-functional teams, product owners, agency stakeholders, engineers, and policy/program staff - to translate public sector business problems into technical solutions, and to clearly communicate model behavior, limitations, and outcomes to both technical and non-technical audiences.
</li></ul><br>
A successful candidate would possess these skills:<br>
<ul><li>
Ability to work independently and collaborate as part of a team</li><li>
Effective written and verbal communication skills</li><li>
Meticulous attention to detail and quality of work product</li><li>
Ability to build and sustain professional relationships</li><li>
Ability to lead projects or workstreams</li><li>
Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment</li><li>
Strong interpersonal skills and professional demeanor</li><li>
Ability to meet deadlines</li><li>
Ability to provide clear guidance to others</li></ul><br>
<b>
The Team</b><br>
<br>
Deloitte's Government &amp; Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, &amp; local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.<br>
<br>
Our AI &amp; Data offering provides a full spectrum of solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively.<br>
<br>
The Project Delivery Talent Model is designed for professionals with specialized skills that align to a current client need. Team members focus on delivering services to clients, without additional expectations related to business development or promotion. Their employment is tied to their role on a project, and they are eligible for a benefits package that is competitive for project delivery-focused professionals.<br>
<br>
<b>Qualifications</b><br>
<br>
Required<b>:
</b><br>
<ul><li>Bachelor's degree required
</li><li>5+ years experience with Python</li><li>
5+ years experience with SQL</li><li>
5+ years experience with Microsoft Azure</li><li>
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future</li></ul></div>

Senior Data Scientist and AI Specialist

Hare and Turtle AI Solutions

<ul><li><b>Programming Expertise</b>: Strong proficiency in Python (primary), with working knowledge of Java or Go.</li><li><b>Generative AI</b>: Hands-on experience with LLMs (e.g., GPT, Claude, Llama), prompt engineering, fine-tuning, and multi-agent orchestration.</li><li><b>Data AI</b>: Proven ability to build and optimize data-driven AI models for analytics, forecasting, anomaly detection, and decision support.</li><li><b>RAG Pipelines</b>: Experience developing retrieval-augmented generation workflows using vector databases (FAISS, Pinecone, Weaviate).</li><li><b>MLOps Practices</b>: Familiarity with CI/CD for ML models, monitoring, drift detection, and deployment automation.</li><li><b>Cloud Platforms</b>: Strong knowledge of AWS, Azure, or GCP for deploying and scaling AI workloads.</li><li><b>API &amp; Microservices</b>: Ability to design and integrate AI models into enterprise applications via APIs and microservices.</li><li><b>Security &amp; Compliance</b>: Understanding of responsible AI practices including bias detection, PII handling, guardrails, and auditability.</li><li><b>Collaboration &amp; Communication</b>: Strong stakeholder management, cross-functional collaboration, and clear communication of technical concepts.</li></ul><p>Pay: $60.00 - $75.00 per hour</p><p>Work Location: In person</p>

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