| Job Description: |
Role Overview
We are looking for an experienced AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver high-impact AI/ML (and GenAI, where applicable) solutions end-to-end. You will blend applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade systems that are scalable, secure, observable, and aligned to business KPIs.
This role is ideal for engineers who enjoy operating at the intersection of data + models + systems + real users, and who can thrive in ambiguous, fast-moving environments
Key Responsibilities
1) Use-Case Discovery & Forward Deployment
• Partner with stakeholders (business/product/customers) to identify and shape AI opportunities into well-defined use cases with success metrics, constraints, and rollout plans.
• Run workshops and technical discovery to assess feasibility, data readiness, integration needs, and operational risks.
• Drive rapid prototyping, pilot deployments, and iterative improvements based on real user feedback.
2) Applied ML Engineering (Classic ML + Deep Learning)
• Develop and improve ML solutions (classification, regression, ranking, forecasting, anomaly detection, NLP).
• Establish and maintain robust evaluation practices: offline metrics, validation strategies, experimentation, and A/B testing.
• Perform feature engineering, error analysis, model optimization, and performance tuning for production requirements.
3) GenAI / LLM Engineering (If Applicable)
• Build and productionize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategy, embeddings, retrieval tuning, reranking, and response grounding.
• Implement guardrails and reliability patterns: prompt templates, tool/function calling, hallucination reduction, citation strategies, and fallback paths.
• Develop evaluation harnesses for GenAI: quality metrics, regression tests, safety tests, and human-in-the-loop workflows.
4) Productionization (MLOps / LLMOps)
• Package models into scalable services and deploy using Docker/Kubernetes and CI/CD.
• Implement model lifecycle management: model registry, versioning, automated retraining triggers, and governance workflows.
• Build monitoring and observability: drift detection, latency/throughput monitoring, error tracking, alerting, and rollback mechanisms.
5) Systems Integration & Platform Collaboration
• Build integration layers (REST/gRPC APIs, event-driven services) to embed AI capabilities into products and enterprise workflows.
• Collaborate with data engineers to design reliable pipelines and ensure data quality, lineage, and governance.
• Ensure secure and compliant design (PII/PHI handling, RBAC, secrets management, encryption, audit trails).
6) Technical Leadership & Enablement
• Provide technical guidance and mentoring to engineers; lead design reviews and establish best practices.
• Document solutions with architecture diagrams, runbooks, and operational playbooks.
• Create reusable accelerators (templates, libraries, patterns) to scale deployments across teams or customers.
Required Qualifications
• Programming & Scripting
o Languages:
UI Skills using React JS (Primary) If not the Angular
Python (primary for automation, APIs, data pipelines)
• API & Backend Engineering
o REST API development (Spring Boot / FastAPI / Node.js)
o API integration using:
OAuth2 / JWT authentication
API gateways (Azure API Management, Apigee)
o Data exchange formats: JSON, XML
HL7/FHIR (important in healthcare) – Secondary or nice to have
• AI/ML & GenAI Integration
o LLM integration:
Azure OpenAI / OpenAI APIs
o Frameworks: LangChain, Semantic Kernel
o RAG (Retrieval-Augmented Generation)
o Prompt engineering
o Embeddings + vector DBs (Pinecone, Azure Cognitive Search)
• Cloud & Infrastructure
o Azure (preferred in Optum ecosystem):
Azure App Services
Azure Functions (serverless)
Azure Kubernetes Service (AKS)
Azure Storage / Blob / Cosmos DB
o AWS (secondary):
Lambda, ECS/EKS, S3
• Data Engineering & Handling
o Any SQL RDBMS
o NoSQL - MongoDB preferred if not Cosmos DB
Preferred Qualifications (Nice to Have)
• Forward-deployed / customer-embedded delivery experience (consulting, solutions engineering, implementation engineering).
• Infrastructure as Code (IaC)- Terraform / ARM templates / Bicep (Nice to have
• Experience with vector databases and search: Azure AI Search, Elasticsearch/OpenSearch, Pinecone, Weaviate, Milvus.
• Experience with platforms/tools: Databricks/Spark, MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI.
• Experience with Responsible AI: model governance, fairness testing, explainability, audit readiness.
• Domain expertise (optional): healthcare, PBM
Core Skills (What You’ll Use Often)
• Software development: Programming language and database skills
• ML: training, evaluation, feature engineering, error analysis, model serving
• GenAI (optional): RAG, retrieval tuning, prompt orchestration, guardrails, evaluations
• Software Engineering: APIs/microservices, integration, performance optimization
• MLOps/LLMOps: CI/CD, monitoring, drift, versioning, rollout/rollback
• Cloud & Platform: compute/storage/IAM/networking, containers, Kubernetes
• Security: secrets, RBAC, encryption, compliance-aware design
Success Metrics (How We Measure Impact)
• AI solutions shipped to production with clear SLOs (latency, availability, accuracy/quality).
• Demonstrated business uplift (automation rate, cost reduction, cycle time improvement, conversion/retention, defect reduction).
• High adoption and stakeholder satisfaction; reduced friction via reusable deployment patterns.
• Strong operational posture: monitoring coverage, fast incident response, low failure rates.
Desirable Skills: Healthcare domain
Keyword: ~UHC Set 2 - EP Hiring~
Skills: Digital : Deep Learning~Digital : Azure Machine Learning (ML)~Generative AI
Experience Required: 6-8
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