Job Summary

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.

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.