The candidates is responsible for design and deploy enterprise-grade AI solutions (LLMs, RAG, agents) by selecting appropriate models, building data pipelines, and integrating them with cloud platforms (AWS, Azure, GCP).
Lead technical strategies, ensure scalability, manage AI security and hallucination challenges, and bridge business needs with engineering teams.
Key Responsibilities
• System Design & Architecture
Architect end-to-end Generative AI systems, including retrieval-augmented generation (RAG) and vector data systems.
• Model Selection & Tuning
Evaluate and select cutting-edge commercial (e.g., GPT-4) and open-source models, and fine-tune models for domain-specific use cases.
• LLMOps & Pipelines
Establish LLMOps standards for model versioning, evaluation, prompt management, and CI/CD, ensuring robust, production-grade AI.
• Integration & Security
Integrate AI solutions with existing APIs, applications, and databases while enforcing security, privacy, and guardrails to manage hallucinations and adversarial attacks.
• Strategic Leadership
Collaborate with stakeholders to map business challenges to AI solutions and establish AI governance frameworks.
Required Skills & Qualifications
• Technical Expertise
Deep knowledge of NLP, Python, deep learning frameworks (PyTorch / TensorFlow), and AI frameworks like LangChain, Autogen, or CrewAI.
• Cloud & Data Systems
Extensive hands-on experience with AI services on AWS, Azure, or GCP. Expertise in vector databases (e.g., Pinecone, Milvus, Chroma) and embedding techniques.
• GenAI-Specific Skills
Prompt engineering, RAG architectures, fine-tuning LLMs, and vector database design.
• Soft Skills
Solid problem-solving mindset, strategic thinking, and communication skills, with the ability to explain AI concepts to non-technical teams.
• Qualifications
Bachelor’s / Master’s degree in Computer Science, AI, Data Science, or related field.
8–15 years of experience in software engineering, machine learning, or AI roles, wi
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