4.5–8 years of experience in software engineering, with at least 4+ years focused on AI/ML development, including hands ‑ on exposure to generative AI, LLM-based systems, and agentic AI architectures.
Full ‑ time Bachelor ’ s and/or Master ’ s degree in Engineering with a minimum of 60% grade.
Strong programming proficiency in Python, with experience developing production-grade AI/ML and GenAI applications.
ML Frameworks: Hands ‑ on experience with TensorFlow, PyTorch, scikit ‑ learn, Keras, along with experience in LLM/GenAI and agentic AI frameworks such as Hugging Face Transformers, LangChain, or LangGraph/CrewAI/Autogen.
Proficiency in Machine Learning, Deep Learning, Natural Language Processing, Generative AI, and Agentic AI concepts such as tool ‑ use, multi ‑ agent orchestration, and reasoning workflows.
Experience developing, fine ‑ tuning, optimizing, and deploying AI/ML, LLM, and agentic AI models across cloud or on ‑ prem environments.
Knowledge of data processing, feature engineering, and visualization tools (e.g., Pandas, NumPy, SQL).
Ability to work effectively in global, cross ‑ functional teams and manage multiple concurrent deliverables.
Demonstrated experience across the end ‑ to ‑ end product development lifecycle: design, implementation, testing, debugging, deployment, and maintenance of AI systems.
Strong problem ‑ solving skills, analytical thinking, and the ability to innovate and adopt new AI, GenAI, and agentic AI technologies.
Preferred Qualifications
- Experience in delivering scalable, high ‑ performance AI services and applications with excellent quality across cloud or on ‑ prem environments.
- Experience with Large Language Models (LLMs), prompt engineering, retrieval ‑ augmented generation (RAG), and developing agentic AI workflows using frameworks such as LangChain, LangGraph/Crew AI/AutoGen.
- Familiarity with computer vision, NLP, generative AI frameworks, and multimodal model development.
- Exposure to MLOps tools and optimization technologies such as MLflow, Triton, ONNX, TensorRT, model quantization, GPU acceleration, or distributed inference.
- Familiarity with cloud ‑ native deployments on AWS, GCP, or Azure, including experience integrating AI/ML and LLM systems into secure enterprise environments.
