Lead Manager AI Engineering

We are seeking GenAI and Agentic AI Engineering Hands-on Lead or a Manager with a focus on delivery client excellence and innovation. As an experienced Agentic AI Engineer with deep expertise in LLM Azure AI Snowflake and Machine Learning ecosystems you are responsible to design and implement enterprise-grade AI solutions. The ideal will have hands-on experience architecting end-to-end AI/ML systemsfrom data readiness pipeline through Agentic Solutions deployment leveraging cloud-native architecture.

Test Driven Agentic AI Engineering evaluation strategy metric selection ground-truth creation and decisioning on model and prompting approaches. Youll build and validate GenAI/agentic solutions define what good means and ensure solutions are measurably effective and safe before and after launch. You will build the GenAI solution in a production (model choice RAG/agent behaviour prompts and evaluation).

Key Responsibilities:

• Translate business needs into testable GenAI and Agentic Engineering solutions clear outputs and measurable success criteria; define scope boundaries (what the system should not attempt) including risks.
• Run feasibility assessments to choose the right approach: prompting vs RAG vs fine-tuning vs classical ML.
• Select and develop models based on task requirements (reasoning vs extraction vs classification) working with AI Engineering to understand latency/cost and risk profile.
• Design prompting strategies: instruction design few-shot sets structured outputs tool/agent prompts and robustness patterns. This will be implemented as an MVP and iterate based on eval results.
• Establish prompt iteration methodology driven by evals (not anecdotal testing): prompt versioning ablations and change control.
• Define the evaluation plan for GenAI systems and agentic workflows- designing and implementing evaluation from LLM as a judge and ensure evaluation includes fairness and bias considerations where applicable. Define acceptance thresholds and release gates tied to these metrics.
• Own experimentation and model improvements: Run structured experiments (across prompts retrievers chunking models).
• Develop out methods for identifying model failures such as hallucination types retrieval misses instruction-following errors formatting failures etc
• Provide recommendations for improvements grounded in evidence: what to change expected lift and trade-offs.
• Deliver an engineering-ready handoff: prompt packages and versioning approach RAG configuration tool schemas (if agentic) evaluation harness 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.

Qualifications :

• 5-10 years of overall AI/ML experience out if which at least 2 to 3 years of Generative AI solutions.
• Strong background in applied ML data science LLM and Agentic AI Engineering Systems with demonstrated delivery and client facing experience.
• Deep expertise in evaluation design metrics and dataset curation for LLM systems.
• Proven experience in model selection and prompt engineering including structured output and tool-use prompting.
• Strong proficiency in Python and major ML frameworks (PyTorch TensorFlow Scikit-learn).
• Strong experience in LLM fine-tuning RAG Context Engineering Claude Code Open AI Codex Agentic Workflows.
• Strong RAG design choices (chunking embeddings retrieval strategies reranking) and how to evaluate them.
• Must have implemented Agentic AI SDLC
• Working with GenAI on Azure AWS or Snowflake involves leveraging cloud-native AI toolssuch as Azure OpenAI AWS Bedrock or Snowflake Cortexto build or consume intelligent solutions directly on governed data.
• Experience on vibe coding - such as AntiGravity Cursor and VS Code is highly desirable.
• Proven ability to build end-to-end GenAI MVPs in Python (RAG/agents evaluation harness) and prepare them for production handoff.
• Excellent communication and stakeholder management skills with a strategic mindset.

Required Collaboration Model:

• Partner AI engineering for LLM implementation needs by providing clear specs (prompts/tool schemas) eval harnesses and acceptance thresholds.
• Mentor DS/analysts on GenAI evaluation methods labelling operations and scientific rigor.
• With Product and Software Engineers for integrating AI capabilities into platforms and user-facing services.
• With DevOps/Platform Engineers for environment setup monitoring infrastructure and reliability.
• With Data Engineering for designing and accessing upstream data pipelines.

Additional Information :

Thrive & Grow with Us

• Competitive Salary: Your skills and contributions are highly valued here and we make sure your salary reflects that rewarding you fairly for the knowledge and experience you bring to the table.
• Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly providing the right tools mentorship and experiences to fast-track your career.
• Idea Tanks: Innovation lives here. Our Idea Tanks are your playground to pitch experiment and collaborate on ideas that can shape the future.
• Growth Chats: Dive into our casual Growth Chats where you can learn from the bestwhether its over lunch or during a laid-back session with peers its the perfect space to grow your skills.
• Snack Zone: Stay fuelled and inspired! In our Snack Zone youll find a variety of snacks to keep your energy high and ideas flowing.
• Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives shoutouts and the chance to see your ideas come to life as part of our reward program.
• Fuel Your Growth Journey with Certifications: Were all about your growth! Enhance your expertise with company-sponsored certifications in AI Data Science Cloud and Analytics technologies.

Remote Work :

Yes

Employment Type :

Full-time