Key Responsibilities :
- Lead end-to-end delivery of Generative AI solutions for clients, particularly in manufacturing and administrative domains.
- Architect and implement AI / ML models using advanced techniques in NLP, LLMs (Large Language Models), and generative technologies tailored to specific industry use cases.
- Engage directly with clients to understand business needs, challenges, and objectives, and translate them into actionable technical solutions.
- Mentor and guide junior consultants and technical teams in best practices for GenAI development, deployment, and lifecycle management.
- Collaborate cross-functionally with data engineers, product managers, DevOps, and business stakeholders to ensure smooth integration and deployment of AI solutions.
- Ensure solutions are robust, scalable, secure, and compliant with industry and organizational standards.
- Stay current with the latest GenAI trends, tools, frameworks, and research to continuously drive innovation and elevate solution quality.
- Support pre-sales, proposals, and client pitches as a technical SME in GenAI, contributing to solution design, estimations, and presentations.
Required Skills & Qualifications :
Minimum 4 years of experience in AI / ML engineering, with at least 12 years focused on GenAI technologies.Proven experience in designing and delivering LLM-powered solutions (e.g., using OpenAI, Hugging Face Transformers, LangChain, RAG architecture, etc.).Proficient in Python, with experience using AI / ML libraries such as TensorFlow, PyTorch, spaCy, or similar.Strong experience in NLP, prompt engineering, and working with generative models (e.g., GPT, LLaMA, Claude).Deep understanding of cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).Excellent communication and stakeholder engagement skills, with the ability to interact with both technical and non-technical audiences.Ability to lead projects, mentor team members, and collaborate in a fast-paced, agile environment.Preferred Qualifications :
Experience in industry-specific GenAI use cases in manufacturing, supply chain, or enterprise productivity tools.Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and RAG (Retrieval-Augmented Generation) pipelines.Exposure to MLOps and CI / CD pipelines for model deployment and monitoring.Contributions to open-source GenAI projects or published research work.(ref : hirist.tech)