AI/ML & Forward Deployed Engineer

Role Name: AI/ML & Forward Deployed Engineer (8+ Years)

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

• Languages:

• UI Skills using React JS (Primary) If not the Angular

• Python (primary for automation, APIs, data pipelines)

API & Backend Engineering

• REST API development (Spring Boot / FastAPI / Node.js)

• API integration using:

• OAuth2 / JWT authentication

• API gateways (Azure API Management, Apigee)

• Data exchange formats: JSON, XML

• HL7/FHIR (important in healthcare) – Secondary or nice to have

AI/ML & GenAI Integration

• LLM integration:

• Azure OpenAI / OpenAI APIs

• Frameworks: LangChain, Semantic Kernel

• RAG (Retrieval-Augmented Generation)

• Prompt engineering

• Embeddings + vector DBs (Pinecone, Azure Cognitive Search)

Cloud & Infrastructure

• Azure (preferred in Optum ecosystem):

• Azure App Services

• Azure Functions (serverless)

• Azure Kubernetes Service (AKS)

• Azure Storage / Blob / Cosmos DB

• AWS (secondary):

• Lambda, ECS/EKS, S3

Data Engineering & Handling

• Any SQL RDBMS

• 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.