Lead - AI Engineering

This is a fully remote position, open to applicants in India.

📋 Description

• Transform business requirements into testable solutions in GenAI and Agentic Engineering, ensuring clear outputs and measurable success criteria.

• Establish scope limitations, identify risks, and clarify what systems should refrain from attempting.

• Conduct feasibility assessments to determine the most suitable approaches, including prompting, RAG, fine-tuning, or classical ML methods.

• Choose and develop models according to task specifications, latency, cost, and risk profiles.

• Create prompting strategies that encompass instruction design, few-shot sets, structured outputs, tool/agent prompts, and robustness patterns.

• Construct MVPs and refine them based on assessment outcomes.

• Set up a prompt iteration methodology that includes versioning, ablations, and change control.

• Formulate evaluation plans for GenAI systems and agentic workflows, incorporating LLM-as-judge evaluations and considerations for fairness and bias.

• Establish acceptance criteria and release checkpoints linked to evaluation metrics.

• Execute structured experiments involving prompts, retrievers, chunking, and models.

• Detect model failures such as hallucinations, retrieval misses, instruction-following errors, and formatting issues.

• Suggest evidence-based enhancements with anticipated improvements and associated trade-offs.

• Provide engineering-ready handoffs, including prompt packages, RAG configurations, tool schemas, evaluation harnesses, datasets/ground truth, metric definitions, and go/no-go gates.

• Design scalable and secure Agentic AI architectures adhering to best practices in data engineering, MLOps, and LLMOps.

• Collaborate with AI Engineering, Data Science/analysts, Product, Software Engineering, DevOps/Platform Engineering, and Data Engineering teams.

• Guide data scientists and analysts on GenAI evaluation techniques, labeling operations, and maintaining scientific rigor.

⛳️ Requirements

• 5–10 years of comprehensive experience in AI/ML.

• Minimum of 2–3 years specializing in Generative AI solutions.

• Strong foundation in applied ML, data science, LLM, and Agentic AI Engineering systems.

• Proven track record in delivery and client-facing roles.

• In-depth knowledge of evaluation design, metrics, and dataset curation for LLM systems.

• Demonstrated expertise in model selection and prompt engineering, including structured output and tool-use prompting.

• High proficiency in Python and major ML frameworks, including PyTorch, TensorFlow, and Scikit-learn.

• Solid experience in LLM fine-tuning, RAG Context Engineering, Claude Code, OpenAI Codex, and Agentic Workflows.

• Comprehensive knowledge of RAG design encompassing chunking, embeddings, retrieval strategies, reranking, and evaluation.

• Must have implemented the Agentic AI Software Development Life Cycle (SDLC).

• Experience with Generative AI on Azure, AWS, or Snowflake, including Azure OpenAI, AWS Bedrock, or Snowflake Cortex.

• Familiarity with vibe coding tools such as AntiGravity, Cursor, and VS Code is highly desirable.

• Proven capability to develop end-to-end GenAI MVPs in Python and prepare them for production handoff.

• Exceptional communication and stakeholder management skills with a strategic outlook.

🏝️ Benefits

• Competitive salary.

• Dynamic career advancement opportunities.

• Access to tools, mentorship, and experiences for professional development.

• Idea Tanks for proposing, experimenting, and collaborating on innovative ideas.

• Growth Chats for learning and skill enhancement.

• Snack Zone offering a variety of snacks.

• Recognition and rewards program, including Hive-Fives and shoutouts.

• Company-sponsored certifications in AI, Data Science, Cloud, and Analytics technologies.