Team Lead - AI & Data Science

Designation : Team Lead - AI & Data Science

Required Experience: 5-6 years overall with AI/ML, data science or applied AI; at least 5 years of hands-on AI/ML/data-science work and 2+ years of technical/team leadership.

Job Description:

Aventior is looking for a hands-on Team Lead - AI & Data Science to lead customer-facing AI and analytics engagements from discovery through production and ongoing improvement.

The role combines technical direction, people leadership, customer communication and delivery ownership. You will turn business problems into clear requirements, assess feasibility and data readiness, shape pragmatic AI/GenAI solutions, guide engineers and data scientists, and deliver secure, measurable outcomes in production.

The strongest candidate is equally comfortable discussing business value with a customer, explaining technical options and trade-offs, reviewing code or model quality, and managing scope, risks and delivery commitments.

Key Responsibilities:

1. Customer engagement, discovery and requirement gathering -

●Lead customer and stakeholder discovery sessions, interviews and workshops to understand business goals, users, existing workflows, pain points, data sources, constraints and success criteria.

●Translate business needs into clear problem statements, functional and non-functional requirements, user stories, acceptance criteria, assumptions, dependencies and measurable KPIs.

● Assess data availability, data quality, integration constraints, privacy/security requirements and technical feasibility before recommending an approach.

● Present solution options and trade-offs in business-friendly language, including build-versus-buy, model selection, expected value, risks, cost, latency and operational implications.

● Lead proof-of-concept planning, demos, review meetings and customer sign-off; manage clarifications, scope changes, priorities and expectation setting throughout the engagement.

2. AI solution architecture and technical leadership

● Own solution design across data ingestion and preparation, model or LLM selection, retrieval, orchestration, APIs, integrations, cloud infrastructure and operational monitoring.

● Choose fit-for-purpose approaches for the business problem rather than defaulting to a particular model, vendor, framework or architecture pattern.

● Establish engineering standards through design reviews, code reviews, testing practices, reusable components, documentation and secure development practices.

● Remain hands-on for critical design decisions, prototypes, complex debugging and performance/cost optimization while enabling the team to execute independently.

3. Generative AI, LLMs, RAG and agentic workflows

● Design and deliver LLM-powered applications using commercial APIs and/or managed or open models; apply prompt design, structured outputs, tool/function calling and workflow orchestration as appropriate.

● Build and improve RAG pipelines, including document ingestion, chunking, embeddings, vector or hybrid retrieval, reranking, access-aware retrieval, source attribution and grounded answer generation.

● Select and apply agentic patterns only where they add value; define tool permissions, bounded actions, state transitions, approval steps, retries and safe failure behavior.

● Create systematic evaluation approaches for task success, groundedness, retrieval relevance, factuality, safety and user experience; use representative test sets, regression tests and human review where needed.

●Improve quality, latency and cost through retrieval/model comparisons, prompt and context tuning, caching, routing, token-usage controls and production feedback loops.

4. Applied machine learning and data science

● Guide the framing, development and validation of appropriate ML/statistical solutions such as classification, regression, forecasting, anomaly detection, clustering, NLP or optimization.

● Establish baselines and evaluation criteria; interpret results, investigate failure modes, check for data leakage and communicate limitations and confidence clearly.

● Partner with data engineers and analysts on data profiling, feature engineering, data pipelines, dataset quality, reproducibility and reliable access to data.

● Guide specialist computer-vision, OCR/intelligent document processing or multimodal work when required by the project portfolio; deep specialist research methods are not a blanket requirement for this role.

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