AI/ML & Forward Deployed Engineer

Job description

**TCS Hiring |Forward Deployed Engineer |** Hyd/Noida/Chennai/Pune/Bangalore **Greetings from Tata Consultancy Services (TCS)!** Backend – Python, RestAPI Developer Frontend- React.Js AI/ML (working exp with various LLM), Agentic AI with Azure Devops cloud Exp Range- 6 to 8 **Role Name: AI/ML & Forward Deployed Engineer (6+ 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) - Fast API Development (in Python) - 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