7 months ago
Bengaluru, IndiaEntry Level
Responsibilities
- Build LLM-powered operational dashboards using Claude, GPT-4, Gemini, data visualization tools, and custom frontends.
- Develop prompt pipelines and RAG workflows that surface AI-driven insights for store operations, maintenance, and leadership.
- Monitor and evaluate deployed AI/ML models using accuracy, recall, F1, business KPIs, feedback loops, fine-tuning, prompt engineering, and A/B testing.
- Build and deploy AI agents for escalation handling, store operations, maintenance, workforce optimization, and new store openings.
- Define agent architectures, tool integrations, memory strategies, escalation rules, reliability controls, and auditability standards.
- Build and maintain ETL/ELT pipelines ingesting data from POS, ERP, IoT, ticketing, and SaaS systems.
- Use cloud data warehouses and orchestration tools to support data quality, lineage, governance, analytical workloads, and ML workloads.
- Translate retail business problems into ML/AI solutions and support rollout through documentation and training for non-technical staff.
Requirements
- 1–3 years of professional experience in data engineering, ML engineering, or a related AI/software role.
- Proficiency with Python, pandas, NumPy, scikit-learn, and FastAPI or Flask.
- Experience with LLM APIs such as Anthropic Claude, OpenAI GPT, Google Gemini, or Mistral, including their capabilities and limitations.
- Knowledge of prompt engineering, RAG pipelines, and agent frameworks such as LangChain, LangGraph, CrewAI, or equivalent.
- SQL experience and working knowledge of Snowflake, BigQuery, or Redshift.
- Experience with dbt, Airflow, Prefect, or equivalent data pipeline tools.
- Familiarity with Pinecone, Weaviate, ChromaDB, or other vector databases.
- Dashboard or visualization experience with Streamlit, Tableau, Power BI, Metabase, or similar tools.
- Experience with Git, CI/CD pipelines, and basic ML model lifecycle management.
- Ability to work cross-functionally with store managers, operations leads, executives, and other non-technical stakeholders.
- Strong problem-solving, communication, adaptability, attention to detail, and responsible-AI awareness.
- Prior SaaS experience is strongly preferred; retail, supply chain, brick-and-mortar technology, multimodal AI, agentic frameworks, MCP, SAP Retail, Oracle Retail, Manhattan Associates, Kafka, Kinesis, internal tooling, AI copilots, or model governance experience are preferred or nice to have.
Benefits
- Hybrid work arrangement with on-site and remote work.
- Full-time employment in a junior-level role.
- Mentorship from experienced data and ML engineers with a fast-track growth path.
- Hands-on work with LLM APIs and agentic AI architectures from day one.
- Direct impact on operations serving thousands of stores and millions of customers.
- Collaborative, cross-functional SaaS-style product culture in a retail technology environment.
Tech Stack
Amazon RedshiftApache AirflowApache KafkadbtFastAPIFlaskGitGoogle BigQueryMetabaseNumPyPandasPythonReactscikit-learnSnowflakeSQL
Categories
AI ApplicationsData Engineering
About Lenskart
Lenskart sells prescription eyeglasses, sunglasses, and contact lenses through an omnichannel model—e-commerce apps plus company-owned and franchised retail stores. It designs and manufactures eyewear and provides eye tests and fitting services for consumers across India and select international markets, including Singapore and the Middle East. Founded in 2008 and headquartered in Gurgaon, it is privately held.