AI/ML Engineer

Job Description

We are seeking an experienced and driven Senior AI/ML Engineer with 5-8 years of experience in AI/ML – Predictive ML, GenAI and Agentic AI. The ideal candidate should have a strong background in developing and deploying machine learning models, as well as a passion for innovation and problem-solving.

• As an AI/ML Solution Engineer, build AI/ML, Gen AI, Agentic AI empowered practical in-depth solutions for solving customer’s business problems.
• As an AI/ML Solution Engineer, design, develop, and deploy machine learning models and algorithms.
• Conduct research and stay up-to-date with the latest advancements in AI/ML, GenAI, and Agentic AI.
• Lead a team of junior AI engineers, providing direction and support.

Requirements

• Bachelor’s or Master’s degree in computer science / AIML / Data Science.
• 5 to 8 years of overall experience and hands-on experience with the design and implementation of Machine Learning models, Deep Learning models, and LLM models for solving business problems.
• Proven experience working with Generative AI technologies, including prompt engineering, fine-tuning large language models (LLMs), embeddings, vector databases (e.g., FAISS, Pinecone), and Retrieval-Augmented Generation (RAG) systems.
• Expertise in Python (NumPy, Scikit-learn, Pandas), TensorFlow, PyTorch, transformers (e.g., Hugging Face), or MLlib.
• Experience in working on Agentic AI frameworks like Autogen, Langgraph, CrewAI, Agentforce etc
• Expertise in cloud-based data and AI solution design and implementation using GCP / AWS / Azure, including the use of their Gen AI services.
• Good experience in building complex and scalable ML and Gen AI solutions and deploying them into production environments.
• Experience with scripting in SQL, extracting large datasets, and designing ETL flows.
• Excellent problem-solving and analytical skills with the ability to translate business requirements into data science and Gen AI solutions.
• Effective communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.