Job Overview

Birlasoft is hiring a Generative AI Lead for a full-time hybrid role with opportunities across Noida, Pune, Bengaluru, Chennai, and Mumbai. This position is designed for an experienced AI engineering professional who can take Generative AI solutions beyond experimentation and proof-of-concept stages and turn them into reliable, scalable applications.

The role combines Python engineering, large language model frameworks, cloud platforms, data modernization, API integration, frontend development, retrieval-augmented generation, model fine-tuning, LLMOps, and responsible AI. The successful candidate will be expected to build GenAI applications from the ground up, integrate existing AI models, and create practical solutions for enterprise use cases.

Key Responsibilities

• Design, develop, and productionize Generative AI applications using modern large language models and supporting frameworks.
• Build Python-based applications, automation utilities, and AI engineering workflows suitable for enterprise environments.
• Move GenAI solutions beyond PoCs by applying production engineering practices, quality controls, monitoring, and scalable frameworks.
• Develop solutions using LLM orchestration and application frameworks such as AutoGen, CrewAI, LangGraph, LlamaIndex, and LangChain.
• Design architectures capable of processing large volumes of structured and unstructured data.
• Develop multimodal AI applications involving text, vision, and speech capabilities.
• Fine-tune Small Language Models for domain-specific datasets and business requirements.
• Apply parameter-efficient fine-tuning approaches to adapt models for targeted use cases.
• Build retrieval-based AI applications using RAG and Modular RAG architectures.
• Support data modernization initiatives that prepare enterprise data for GenAI applications.
• Integrate AI applications with enterprise systems through REST, SOAP, and other API protocols.
• Develop user-facing AI experiences using React, Streamlit, AG Grid, and JavaScript.
• Deploy and manage GenAI applications across Azure, Google Cloud Platform, and AWS environments.
• Apply LLMOps practices for model deployment, monitoring, lifecycle management, and operational reliability.
• Implement responsible AI practices and consider ethical, security, and regulatory requirements during solution development.

Required Skills

Strong Python programming is central to this position. Candidates should be able to use Python for GenAI application development, automation, integration, and production engineering rather than only for experimentation.

Hands-on knowledge of LLM application frameworks is required. Experience with AutoGen, CrewAI, LangGraph, LlamaIndex, or LangChain is relevant to the role. Candidates should understand how these frameworks can be used to coordinate models, tools, workflows, retrieval components, and AI agents.

Experience with productionizing GenAI solutions is particularly important. The role expects candidates to understand the difference between a prototype and a production-ready application, including reliability, scalability, monitoring, testing, and maintainability.

Strong knowledge of large-scale data architecture is required, including the ability to work with structured and unstructured information. Experience designing data flows for AI applications and modernizing existing data environments is valuable.

Required Technologies

• Python
• Large Language Models (LLMs)
• AutoGen
• CrewAI
• LangGraph
• LlamaIndex
• LangChain
• RAG
• Modular RAG
• Small Language Models (SLMs)
• PEFT
• QLoRA
• LoRA
• LLMOps
• Responsible AI
• React
• Streamlit
• AG Grid
• JavaScript
• Azure
• Google Cloud Platform (GCP)
• AWS
• REST APIs
• SOAP
• API integration
• Multimodal AI
• Text models
• Vision models
• Speech models
• Data modernization
• GenAI application development

Preferred Skills

Candidates with strong experience in AI agent development, multimodal applications, domain-specific model adaptation, and enterprise AI architecture will be well aligned with the role. Experience across more than one major cloud platform is also useful because the position specifically references Azure, GCP, and AWS.

Practical knowledge of fine-tuning methods such as PEFT, QLoRA, and LoRA is preferred. Experience with RAG and Modular RAG can help candidates build applications that combine enterprise information with language model capabilities.

Experience using frontend technologies such as React, Streamlit, AG Grid, and JavaScript is valuable for creating usable AI applications rather than backend-only prototypes.

Soft Skills

The role requires strong technical ownership and the ability to turn business requirements into working AI products. Candidates should be comfortable solving complex engineering problems, evaluating different AI approaches, and making practical technology decisions.

Clear communication is important when working with technical and business stakeholders. A strong Generative AI Lead should also be able to explain model behavior, application architecture, limitations, and operational considerations in language appropriate for different audiences.

A continuous-learning mindset is useful because GenAI frameworks, models, cloud services, and engineering practices are evolving quickly. Candidates should be comfortable experimenting with new approaches while maintaining production-quality engineering standards.

Benefits of Working in this Role

This role provides an opportunity to work on enterprise Generative AI engineering rather than limiting the work to model experimentation. The position covers the complete application journey, from data preparation and model integration through deployment, monitoring, user experience, and operational improvement.

Professionals can also deepen their experience across multiple cloud platforms, LLM frameworks, AI agents, RAG architectures, multimodal AI, model fine-tuning, LLMOps, and responsible AI practices. This combination can support future progression into AI technical leadership, GenAI architecture, AI platform engineering, or enterprise AI consulting roles.

Work Mode

The job is explicitly listed as Hybrid.

Location

The position is available in Noida, Pune, Bengaluru, Chennai, or Mumbai, India, according to the provided job description.

Who Should Apply

This opportunity is suitable for experienced AI and software engineering professionals with 6 to 8 years of experience who have hands-on exposure to Generative AI application engineering. Candidates should be comfortable building production-oriented solutions with Python, LLM frameworks, cloud platforms, data architectures, APIs, and modern AI techniques.

This is not a fresher role. Applicants should be able to demonstrate practical experience beyond basic AI experimentation, particularly in building scalable applications, integrating existing models, developing AI agents, implementing RAG solutions, or operationalizing GenAI systems.

Career Growth

Experience in this position can help professionals progress toward roles such as Lead AI Engineer, Generative AI Architect, AI Solutions Architect, LLMOps Engineer, AI Platform Lead, or Enterprise AI Technical Consultant. Exposure to cloud-native deployment, data modernization, responsible AI, model adaptation, and production engineering can broaden long-term technical leadership opportunities.

Application Advice

Highlight real-world GenAI projects on your resume, especially projects that moved from prototype to production. Clearly mention your experience with Python, LLM frameworks, RAG, AI agents, cloud platforms, and API integration.

If you have worked with AutoGen, CrewAI, LangGraph, LlamaIndex, LangChain, PEFT, QLoRA, LoRA, or LLMOps, list the technologies alongside the work you actually performed. Explain how you handled data, deployment, monitoring, scalability, and integration rather than providing a technology-only list.

Also highlight experience with Azure, GCP, AWS, React, Streamlit, JavaScript, multimodal AI, or responsible AI where applicable. Keep your application accurate and avoid claiming expertise in tools or frameworks that you have not used professionally.