Generative AI - Engineer

BharatGen; Mumbai; On-site; INR 800,000 - 1,500,000; Full time

Job summary

A dynamic technology firm in Mumbai seeks a Generative AI Engineer to develop and fine-tune AI models for diverse business applications. The ideal candidate will possess 2-5 years of experience in applied machine learning, strong skills in Python, and a hands-on approach with various AI frameworks. Responsibilities include overseeing the full lifecycle of model development, optimizing performance, and collaborating with cross-functional teams to deploy scalable AI solutions. A proactive mindset and ability to thrive in a startup environment are essential.

Qualifications

• 2 to 5 years of industry experience in applied machine learning or AI development.
• Hands-on experience with ML frameworks and libraries.
• Strong foundation in computer science fundamentals.

Responsibilities

• Develop and train generative AI models across various modalities.
• Fine-tune models for specific tasks.
• Maintain data curation and training pipelines.

Skills

Python programming

Problem-solving

Team player

Interpersonal skills

Education

Bachelor’s or Master’s in Computer Science, AI/ML, Data Science

Tools

TensorFlow

PyTorch

GPU/TPU training libraries

Distributed training tools

Job description

We are looking for a Generative AI – Engineer to join our growing team to help design, build, fine-tune, and deploy cutting – edge generative AI models and agentic systems. You will work on the full lifecycle of foundational model development – involving both large and small language models (LLMs and SLMs) – to create scalable AI solutions that address diverse needs across different business domains. This role is ideal for proactive individuals with a strong foundation in machine learning and an experimental mindset, who are passionate about driving transformative advancements in generative AI from research to real-world production impact.

Key Responsibilities

• Develop and train foundational generative AI models across modalities such as text-to-text, text-to-speech, automatic speech recognition, and vision language.
• Fine-tune and adapt models for specific tasks and domains.
• Build and maintain pipelines for data curation, preprocessing, training, evaluation, and continuous improvement of models.
• Implement debugging, CI/CD, and observability to ensure reliability and efficiency across the development lifecycle.
• Develop retrieval-augmented generation (RAG) pipelines and optimize prompt engineering strategies.
• Optimize training and inference performance through quantization, distributed training/inference, GPU/TPU acceleration.
• Monitor, benchmark, and improve model performance with a focus on accuracy, efficiency, and reducing hallucinations.
• Collaborate with cross-functional teams to build robust AI stacks and integrate them seamlessly into production pipelines for deployment.
• Document technical processes, AI model architectures, and experimental results, while maintaining well- structured, version-controlled code repositories.
• Stay current with advancements in transformer architectures, open-source releases, and AI tooling.
• Bachelor’s or Master’s in Computer Science, AI/ML, Data Science or any related field with 2 to 5 years of industry experience in applied machine learning or AI development.
• Proficiency in Python programming with solid foundation in computer science fundamentals such as data structures and algorithms.
• Strong problem-solving skills and demonstrated ability to lead projects.
• Hands-on experience with a few of the tools listed below:
• One or more model libraries and ML frameworks such as TensorFlow, PyTorch, HF Transformers, NeMo, etc.
• AI application libraries and orchestration frameworks such as DSPy, Langgraph, Langchain, Llamaindex, etc.
• GPU/TPU based training and inference using libraries such as vLLM.
• Distributed training tools such as SLURM, Ray, Pytorch DDP, NCCL, etc.
• Version control, observability systems, and MLOps tools such as Git, DVC, W&B, MLFlow, KubeFlow, etc.
• Data analysis and curation tools such as Dask, Milvus, Apache Spark, Numpy, etc.
• Chunking, embeddings, vector databases (e.g., Pinecone, Weaviate, Milvus), and retrieval-augmented generation (RAG).
• Model context protocol (MCP), Agent to Agent (A2A), and Agent Communication Protocol (ACP).
• Team player with excellent interpersonal skills and ability to collaborate effectively with remote team members.
• Go-getter attitude and ability to flourish in a fast-paced, startup environment.
• Prior experience of building and deploying LLMs or SLMs, experience with multimodal models, and track record of contributions to open-source AI/ML projects would be a big plus.