{"id":2723336,"url":"https://alion.io/job/lenskart-aiml-engineer-ds","title":"AI/ML Engineer (DS)","company":{"id":689,"name":"Lenskart","domain":"lenskart.com","url":"https://alion.io/company/lenskart","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"B","score":77,"open_postings":65,"ghost_share":0.308,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":117,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":12500,"max_usd":31000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"dbt","optional":false},{"name":"Edge AI","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"GPT-4","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Metabase","optional":false},{"name":"Mistral","optional":false},{"name":"NumPy","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Power BI","optional":false},{"name":"Prefect","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Tableau","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Anomaly Detection","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Function Calling","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Tool Use","optional":true}],"status":"live","first_seen_at":"2026-04-09T09:14:14Z","employer_posted_date":"2026-04-09","last_verified_at":"2026-10-17T23:34:17Z","board_verified":true,"closed_at":null,"days_open":191,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":190},"description":"ROLE OVERVIEW\nWe are looking for a motivated and technically curious Junior AI/ML Engineer - Data Engineer to join our Retail Technology team. This is a high-impact, cross-functional role at the intersection of artificial intelligence, data engineering, and retail operations. The ideal candidate comes from a SaaS product background, has a solid foundation in ML and data pipelines, and is excited to apply cutting-edge AI (including LLM-based solutions such as Claude, GPT, Gemini, or similar) to real-world retail challenges - from store operations and maintenance to CEO-level escalation management and new store openings.\nJob Title\nJunior AI/ML Engineer - Data Engineer (Retail Intelligence)\nDepartment\nCEO's Office\nLocation\nHybrid (On-site + Remote)\nEmployment Type\nFull-Time | Junior Level (1-3 years exp.)\nIndustry\nRetail (Preferred: prior SaaS background)\nSalary Band\nCompetitive - Based on experience\nLLMs / GenAI\nData Engineering\nML Models\nRetail Ops\nAI Agents\nSaaS Background\nAbout the Role\nThis role bridges the gap between data infrastructure and intelligent AI-driven applications. You will work closely with retail operations, store maintenance, remote optimization (Optum), NSO (New Store Opening) teams, and senior leadership to build pipelines, AI agents, and dashboards that drive decisions at speed and scale. You will be hands-on with large language model (LLM) APIs - such as Claude, GPT-4, Gemini, or similar - to create intelligent agents and accuracy-enhanced workflows.\nKey Responsibilities\nLLM-Powered Dashboard Development\n\nDesign and build operational dashboards using LLM APIs (e.g., Claude, GPT-4, Gemini) and data visualization tools (e.g., Tableau, Power BI, Streamlit, or custom React/Python frontends).\nIntegrate LLM outputs into live dashboards to surface AI-driven insights for store ops, maintenance, and leadership teams.\nCollaborate with business stakeholders to translate retail KPIs into automated dashboard metrics.\nBuild prompt pipelines and RAG (Retrieval Augmented Generation) workflows to power dashboard intelligence.\n2. Model Accuracy & Improvement\nMonitor and evaluate the performance of deployed ML/AI models against retail-specific metrics (accuracy, recall, F1, business KPIs).\nImplement feedback loops, fine-tuning strategies, and prompt engineering improvements to iteratively improve model accuracy.\nConduct A/B testing and comparative evaluations of model versions.\nWork with domain experts to curate retail-specific training data and validation sets.\n3. AI Agent Development\nBuild and deploy autonomous AI agents using LLM frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, or custom orchestration) with models such as Claude, GPT-4, or Gemini for:\n- CEO Escalation Handling: Triage, summarize, and route escalated issues to the correct teams with context and recommended actions.\n- Store Operations (Store Ops): Automate routine queries, anomaly alerts, and operational reporting for store managers.\n- Store Maintenance: Proactive issue detection, ticket creation, and maintenance scheduling agents.\n- Remote Optum: Agents that optimize staffing, remote task assignments, and operational efficiency recommendations.\n- NSO (New Store Opening): End-to-end checklists, document generation, vendor coordination, and readiness tracking agents.\nDefine agent architectures, tool integrations, memory strategies, and escalation rules.\nEnsure agent outputs are reliable, auditable, and aligned with compliance/retail standards.\n4. Data Engineering & Pipeline Development\nBuild and maintain robust ETL/ELT pipelines to ingest data from POS systems, ERP platforms, IoT sensors, ticketing tools, and SaaS platforms.\nWork with cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestration tools (Airflow, dbt, Prefect).\nEnsure data quality, lineage, and governance for all pipelines feeding AI/ML systems.\nDesign schemas and data models optimized for analytical and ML workloads in a retail context.\n5. Retail Domain Applications\nDevelop AI use cases tailored to retail operational functions including store ops, maintenance workflows, NSO launch tracking, and leadership reporting.\nTranslate retail business problems into well-scoped ML/AI problem statements.\nSupport rollout of AI tools to non-technical retail staff with clear documentation and training materials.\nRequired Skills & Qualifications\nTechnical Skills\nPython proficiency (pandas, NumPy, scikit-learn, FastAPI/Flask for APIs).\nExperience with LLM APIs - such as Anthropic Claude, OpenAI GPT, Google Gemini, Mistral, or similar - and understanding of their capabilities and limitations.\nPrompt engineering, RAG pipelines, and agent frameworks (LangChain / LangGraph / CrewAI or equivalent).\nSQL and working knowledge of cloud data warehouses (Snowflake / BigQuery / Redshift).\nData pipeline tools: dbt, Airflow, Prefect, or equivalent.\nFamiliarity with vector databases (Pinecone, Weaviate, ChromaDB) for RAG architectures.\nDashboard/visualization experience: Streamlit, Tableau, Power BI, Metabase, or similar.\nVersion control with Git and experience in CI/CD pipelines.\nBasic ML model lifecycle management (training, evaluation, deployment, monitoring).\nDomain & Experience\n1-3 years of professional experience in data engineering, ML engineering, or a related AI/software role.\nPrior experience in a SaaS company is strongly preferred - you understand product thinking, iterative development, and API-driven architectures.\nExposure to or interest in retail operations, supply chain, or brick-and-mortar technology.\nAbility to work cross-functionally with non-technical stakeholders (store managers, operations leads, C-suite).\nSoft Skills\nStrong problem-solving mindset with a bias for action.\nClear written and verbal communication - can translate AI concepts into plain language for retail teams.\nComfortable operating in ambiguous, fast-paced environments with evolving requirements.\nDetail-oriented with a focus on data quality and responsible AI use.\nNice to Have\nExperience with multi-modal AI (image/document understanding for store audits, maintenance photos, etc.).\nExposure to agentic frameworks and tool-use patterns (function calling, MCP, etc.).\nKnowledge of retail-specific systems (SAP Retail, Oracle Retail, Manhattan Associates, etc.).\nExperience with real-time data streaming (Kafka, Kinesis).\nPrior work in building internal tooling or AI copilots for operations teams.\nFamiliarity with responsible AI, bias detection, and model governance frameworks.\nWhat You Will Work On - Use Case Highlights\nDomain\nAI/Data Challenge\nYour Role\nCEO Escalations\nHigh-volume escalations needing fast, intelligent triage and routing\nBuild escalation agent with LLMs - summarize, prioritize, and route with context\nStore Operations\nReal-time ops data across 100s of stores lacking unified visibility\nData pipelines + LLM-powered dashboards and anomaly detection agents\nStore Maintenance\nReactive maintenance leading to store downtime and costs\nPredictive maintenance agents, auto-ticketing, and scheduling workflows\nRemote Optum\nSuboptimal remote workforce task allocation and efficiency tracking\nOptimization models + agent-driven task assignment recommendations\nNSO (New Store Opening)\nComplex, multi-team checklists and vendor coordination for new sites\nNSO readiness agent - document generation, tracker, alert workflows\nWhy Join Us\nWork at the cutting edge of AI applied to one of the world's largest industries - retail.\nHands-on with LLM APIs and agentic AI architectures (Claude, GPT, Gemini, and more) from day one.\nDirect impact on operations serving thousands of stores and millions of customers.\nMentorship from experienced data and ML engineers; fast-track growth path.\nCollaborative, cross-functional team that values curiosity, ownership, and speed.\nSaaS-style product culture in a retail-tech environment - best of both worlds.\nWe are an equal-opportunity employer. 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