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 production with clear **SLOs** (latency, availability, accuracy/qualit