Job Title

About Us: We are looking for a Gen 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. Minimum Qualifications and Experience: - 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. Required Expertise: - 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.