Coforge Hiring Senior Technical Lead - GenAI in Bangalore

Job Overview

Coforge is hiring a Senior Technical Lead in Bangalore to build and productionize GenAI agentic solutions for large-scale production environments. The role focuses on using AI agents to diagnose runtime problems, reason over operational information, execute approved actions, and improve production support productivity while reducing operational risk and cost.

This is a senior engineering opportunity for professionals who can combine strong software development skills with practical machine learning and large language model experience. The position involves designing tool-calling agents, building retrieval-augmented generation pipelines, integrating AI systems with production runtime platforms, and establishing safeguards that support secure and reliable automation.

The successful candidate will work closely with production engineers and application teams to identify operational challenges and turn them into measurable agentic AI solutions. The role requires a balance of experimentation, engineering discipline, security, governance, performance optimization, and business-focused delivery.

Key Responsibilities

• Design and implement agentic AI systems using tool calling, retrieval, structured reasoning, function calling, and secure action execution.
• Apply the MCP protocol when developing agent architectures and establish guardrails based on safety, compliance, and least-privilege access.
• Productionize large language models by creating evaluation frameworks, retrieval workflows, prompt synthesis processes, response validation, and self-correction mechanisms.
• Connect AI agents with observability, incident management, and deployment environments to support diagnostics, runbook execution, remediation, and post-incident reporting.
• Work with production and application teams to identify operational pain points and translate them into agentic AI roadmaps.
• Define measurable objectives related to reliability, risk reduction, cost, usefulness, correctness, and operational impact.
• Build safety controls such as validator models, adversarial prompts, policy checks, deterministic fallbacks, circuit breakers, and rollback mechanisms.
• Optimize AI systems for cost and latency through prompt engineering, context management, caching, model routing, batching, streaming, and parallel tool calls.
• Develop RAG pipelines by curating domain knowledge, validating data quality, maintaining knowledge freshness, and establishing feedback loops.
• Participate in design reviews, maintain engineering rigor, and mentor other engineers on agent architectures, evaluation methods, and safe deployment practices.

Required Skills

Candidates should have at least five years of software development experience in one or more languages such as Python, C/C++, Go, or Java. Strong hands-on experience building and maintaining large-scale Python applications is preferred. The role also requires at least three years of experience designing, architecting, testing, and launching production machine learning systems.

Practical experience with LLM-based applications is essential. This includes API integration, prompt engineering, model fine-tuning or adaptation, RAG, vector retrieval, function calling, and secure tool execution. Candidates should understand both commercial and open-source LLMs and be able to evaluate their capabilities for different production use cases.

A solid understanding of applied statistics, machine learning concepts, algorithms, and data structures is expected. Strong analytical thinking and practical problem-solving are important because the role deals with complex production and reliability challenges.

Preferred Skills

Cloud infrastructure experience is preferred, particularly on AWS. Candidates with experience operating containerized services, serverless applications, data services, workflow orchestration, model serving, and infrastructure-as-code will be well aligned with the preferred profile.

The source job description specifically identifies AWS services and technologies including ECS, EKS, Lambda, S3, DynamoDB, Redshift, Step Functions, SageMaker, Terraform, and CloudFormation as desirable areas of experience. Python, API integration, Java, and PySpark are also relevant skills listed for the role.

Education

The provided job description does not specify a required degree or formal educational qualification. Applicants should therefore rely on their demonstrated professional experience and technical capabilities rather than assuming an education requirement that is not stated in the source.

Experience

The position requires 5+ years of software development experience and 3+ years of experience designing, architecting, testing, and launching production ML systems. Experience should include production model deployment or serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.

Candidates should also have hands-on experience building applications with LLMs, RAG pipelines, and tool-using agents. Experience with large-scale Python applications is preferred, while knowledge of Java or other listed programming languages can also be relevant.

Required Technologies

The primary technologies and technical concepts include Python, PySpark, API integration, Java, C/C++, Go, LLMs, RAG, vector retrieval, function calling, MCP protocol, prompt engineering, model fine-tuning, machine learning, statistics, data structures, and algorithms.

Preferred cloud and infrastructure technologies include AWS, ECS, EKS, Lambda, S3, DynamoDB, Redshift, Step Functions, SageMaker, Terraform, and CloudFormation. The role also involves agent evaluation, model monitoring, data processing, secure tool execution, containerized services, serverless computing, and infrastructure-as-code.

Soft Skills

The role requires strong analytical problem-solving, ownership, urgency, and the ability to communicate complex technical concepts in simple terms. Collaboration is important because the Senior Technical Lead will work with production engineers, application teams, and global stakeholders.

A strong focus on measurable business impact is expected. Candidates should be comfortable taking ownership of complex problems, working across technical disciplines, participating in design discussions, and mentoring peers on emerging AI engineering practices.

Benefits of Working in this Role

The role provides an opportunity to work on production-grade GenAI and agentic AI systems that address real operational challenges. It combines software engineering, machine learning, LLM applications, cloud infrastructure, reliability engineering, and AI governance.

The position also offers exposure to emerging areas such as RAG, tool-using agents, MCP, LLM evaluation, model routing, secure automation, and AI-assisted production operations.

Work Mode

The provided job description does not specify whether the role is onsite, hybrid, or remote. Therefore, the work mode is not assumed from the available information.

Location

The position is based in Bangalore, Karnataka, India. It is relevant to professionals searching for Coforge Jobs in Bangalore, Senior Technical Lead Jobs, GenAI Jobs, Agentic AI Jobs, AI Engineer Jobs, Machine Learning Jobs, Python Jobs, Cloud Jobs, Software Jobs, IT Jobs, and experienced technology leadership opportunities.

Who Should Apply

This role is suitable for experienced software engineers and technical leads who have moved beyond application development into production machine learning and GenAI engineering. Candidates should be comfortable building reliable software systems as well as designing LLM-powered applications.

Professionals with strong Python experience, production ML expertise, RAG implementation experience, agent architecture knowledge, API integration skills, and cloud engineering exposure should consider applying. Candidates who have worked with AWS services, containerized applications, model serving, or infrastructure-as-code may have an additional advantage.

Career Growth

This position can help experienced engineers deepen their expertise in agentic AI architecture, LLM engineering, production machine learning, cloud platforms, AI safety, evaluation, and intelligent automation. The technical leadership responsibilities can also support growth toward broader AI architecture, engineering leadership, and platform strategy roles.

Application Advice

Highlight your hands-on software development experience, especially work involving Python and large-scale production applications. Clearly describe production ML systems you have designed or launched, including model deployment, serving, evaluation, monitoring, data pipelines, and fine-tuning.

Your resume should also identify practical LLM experience such as RAG, vector retrieval, prompt engineering, API integration, function calling, secure tool execution, and agentic workflows. If you have worked with AWS ECS, EKS, Lambda, S3, DynamoDB, Redshift, Step Functions, SageMaker, Terraform, or CloudFormation, include those technologies with relevant project context.

Also emphasize measurable outcomes involving reliability, cost reduction, latency, automation, risk reduction, or operational productivity where applicable. Demonstrate your ability to communicate technical ideas clearly, work with global teams, and mentor engineers.