AI/ML & Forward Deployed Engineer

Tata Consultancy Services; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Tata Consultancy Services (TCS) is seeking an AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver end-to-end AI/ML solutions. The role blends applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade, scalable and secure systems aligned to business KPIs.

This position operates at the intersection of data, models, systems, and real users, thriving in fast-moving environments and ambiguity.

Qualifications

• Proficient in frontend React.js development for UI layers
• Strong Python-based automation and API development with FastAPI
• Experience integrating LLMs and GenAI workflows
• Hands-on with Azure cloud, AWS as secondary
• Knowledge of Docker/Kubernetes for deployment and CI/CD
• Familiar with Retrieval-Augmented Generation (RAG) and vector databases
• Experience with LangChain, Semantic Kernel for AI workflows
• Experience with MLOps, model lifecycle management, observability, and governance

Responsibilities

• Identify AI opportunities with stakeholders and shape use cases with success metrics
• Develop and productionize ML solutions (classic ML, DL, NLP)
• Build and optimize RAG pipelines and document ingestion
• Implement guardrails, tool calls, and grounding for reliable GenAI applications
• Deploy services via Docker/Kubernetes and CI/CD pipelines
• Collaborate with data engineers for robust data pipelines and governance
• Provide technical guidance and create reusable deployment accelerators

Skills

React JS

Python

FastAPI

Azure

Docker

Kubernetes

RAG

LangChain

Semantic Kernel

Tools

Docker

Kubernetes

Azure

AWS

HL7/FHIR

LangChain

Semantic Kernel

Databricks/Spark

MLflow

Kubeflow

Azure ML

SageMaker

Vertex AI

Job description

Greetings from Tata Consultancy Services (TCS)!

Frontend- React.Js

AI/ML (working exp with various LLM), Agentic AI with Azure Devops cloud

Exp Range- 6 to 8

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.
• 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.
• 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.
• 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)
• Fast API Development (in Python)
• HL7/FHIR (important in healthcare) – Secondary or nice to have
• AI/ML & GenAI Integration
• LLM integration:
• Frameworks: LangChain, Semantic Kernel
• RAG (Retrieval-Augmented Generation)
• Azure (preferred in Optum ecosystem):
• AWS (secondary):
• Lambda, ECS/EKS, S3
• 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
• GenAI (optional): RAG, retrieval tuning, prompt orchestration, guardrails, evaluations
• Software Engineering: APIs/microservices, integration, performance optimization

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.