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)
- Rapid 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/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.

📌 AI/ML & Forward Deployed Engineer (Bangalore Metropolitan Area)
🏢 Tata Consultancy Services
📍 Bangalore Metropolitan Area