Description

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

Key Skills

Education

Bachelor's degree