Hi All,
Pls help me with profiles for the below AI/ML Positions
please do not share non local profiles
Position 1:
Role: AI Agent Developer / Agentic AI Developer
Role Summary
Design, build, and scale autonomous AI agents that augment software engineering, DevSecOps, and enterprise developer workflows using modern large language models (LLMs) and agentic architectures.
Key Responsibilities
Design and develop autonomous and semi autonomous AI agents leveraging LLMs and agentic frameworks to solve complex engineering and operational problems.
Build AI-powered developer productivity tools, including assistants for code generation, review, refactoring, and documentation.
Develop and deploy agents that automate coding, testing, CI/CD, DevSecOps, and quality assurance workflows.
Integrate leading LLM platforms and copilots (e.g., GitHub Copilot, Claude, Codex) into enterprise developer toolchains and workflows.
Architect agent orchestration, tool calling, memory, and feedback loops to ensure reliability, security, and scalability.
Partner with platform, security, and engineering teams to ensure secure, compliant, and production ready AI solutions.
Continuously evaluate emerging LLMs, frameworks, and agentic patterns to drive innovation and best practices.
Position:2
Role: Agentic AI Coach (Mid Level)
Role Overview
The Agentic AI Coach will play a hands on role in designing, coaching, and operationalizing agentic AI solutions for healthcare clients. This role bridges AI engineering, consulting delivery, and change enablement, ensuring AI agents are not only built correctly but are adopted, governed, and embedded into real business workflows.
You will work closely with AI architects, healthcare SMEs, and client stakeholders to guide the implementation, orchestration, and responsible use of AI agents across payer operations, analytics, testing, and automation use cases.
Key Responsibilities
Agentic AI Enablement & Coaching
Coach delivery teams and clients on how to design, deploy, and operate agentic AI systems, including task decomposition, autonomy levels, guardrails, and human in the loop patterns.
Guide teams on best practices for agent orchestration, memory, planning, tool usage, and escalation logic.
Support adoption of agentic AI operating models, including agent lifecycle management, monitoring, and continuous improvement.
Solution Delivery & Client Engagement
Partner with AI Architects and Consultants to translate business problems into agent based solutions, particularly across payer workflows (claims, prior auth, care management, analytics, testing, ops).
Participate in client workshops, solution walkthroughs, and demos, explaining agent behavior, outcomes, and limitations in business friendly language.
Support delivery of agent enabled assessments, pilots, and scaled implementations.
Responsible AI & Governance
Ensure agentic solutions align with Optum’s AI governance, security, and compliance standards, including explainability, auditability, and risk controls.
Help teams define agent guardrails, approval thresholds, and failure handling to minimize operational and compliance risk.
Contribute to governance artifacts such as agent design reviews, risk assessments, and usage guidelines.
Platform & Tooling Support
Work with platforms such as Azure OpenAI, orchestration frameworks, and internal Optum agentic platforms to support delivery teams.
Assist in configuring agent monitoring, telemetry, and performance metrics.
Support integration of agents with enterprise systems (APIs, data platforms, workflow tools).
Required Qualifications
4–7 years of experience in AI, data, automation, or digital consulting roles.
Hands on exposure to GenAI / LLM based systems, including prompt design, tool calling, or workflow automation.
Practical understanding of agentic AI concepts: autonomy levels, planning, memory, orchestration, and human in the loop patterns.
Experience working in healthcare payer or provider environments (claims, UM/CM, analytics, ops preferred).
Strong communication skills with the ability to coach both technical teams and business stakeholders.
Preferred Qualifications
Experience supporting or delivering AI pilots, PoCs, or scaled implementations.
Familiarity with Responsible AI, governance, or compliance considerations in regulated industries.
Exposure to Azure, cloud platforms, or MLOps concepts.
Consulting experience within healthcare, insurance, or life sciences.
Position:3
Role Overview
The AI/ML Engineer (Mid Level) will design, build, and deploy modern AI and machine learning solutions for healthcare clients, working closely with consultants, architects, and domain SMEs. This role is focused on applied AI delivery—not research—using cloud native, GenAI ready stacks to solve real payer and provider problems.
You will contribute across the lifecycle: data preparation, model development, GenAI integration, deployment, and optimization, with a strong emphasis on responsible AI, scalability, and production readiness.
Key Responsibilities
AI / ML Solution Development
Build and deploy ML and GenAI solutions using modern frameworks and cloud services.
Develop models across use cases such as NLP, predictive analytics, classification, anomaly detection, and decision support.
Implement LLM based solutions (RAG, prompt engineering, embeddings, agent assisted workflows) under architectural guidance.
Write clean, maintainable, production grade code in Python.
Data & Feature Engineering
Perform data ingestion, feature engineering, and data validation across structured and unstructured healthcare data.
Work with large datasets using distributed processing frameworks where applicable.
Partner with data engineers and SMEs to ensure data quality and relevance.
Deployment & MLOps
Package and deploy models using containerized and cloud native patterns.
Support model monitoring, performance tracking, retraining, and drift detection.
Follow Optum standards for model reviews, documentation, and approvals.
Consulting & Client Delivery
Collaborate with consultants to translate business requirements into technical AI solutions.
Participate in client discussions, solution demos, and working sessions.
Contribute to PoCs, pilots, and scaled implementations.
Responsible AI & Compliance
Ensure solutions meet Responsible AI, security, and compliance requirements.
Implement explainability, traceability, and audit ready artifacts where required.
Support AI governance and review processes as part of delivery.
Required Qualifications
4–7 years of experience in AI/ML engineering or applied data science roles.
Strong hands on experience with Python and ML libraries (e.g., scikit learn, PyTorch, TensorFlow).
Experience building end to end ML solutions from data to deployment.
Practical exposure to GenAI / LLM concepts (prompting, embeddings, RAG, APIs).
Experience working in cloud environments (Azure preferred).
Ability to communicate effectively with both technical and non technical stakeholders.
Preferred Qualifications
Experience in healthcare, insurance, or regulated industries.
Familiarity with Azure ML, Databricks, or similar platforms.
Exposure to agent based or workflow oriented AI systems.
Consulting or client facing delivery experience.
Knowledge of Responsible AI, model governance, or compliance workflows.
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