{"id":3273111,"url":"https://alion.io/job/oolka-team-lead-ai-ml","title":"Team Lead - AI / ML","company":{"id":1527,"name":"Oolka","domain":"oolka.in","url":"https://alion.io/company/oolka","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","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":26000,"max_usd":69000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI 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and ship ML and GenAI products end-to-end, from problem framing and modeling to scalable deployment and monitoring.\n\nResponsibilities: \nOwn the technical roadmap for ML/GenAI initiatives and align it with product goals and delivery timelines.\nLead LLM workflow and agentic system design, including retrieval, tool-use, and evaluation pipelines.\nGuide model development using PyTorch and HuggingFace; enforce code quality, testing, and model evaluation standards.\nOversee MLOps: CI/CD for models, API design, containerization, orchestration, and cost/performance optimization on AWS.\nCollaborate with product, data, and infra teams; run experiments, A/B tests, and error analysis.\nMentor engineers, conduct code/model reviews, and grow team capabilities.\n\nRequirements: \nAt least 5 years of experience with a minimum of one year in technical leadership leading projects or teams.\nGeneral AI/ML Team Lead requirements: roadmap ownership, sprint planning, stakeholder communication, mentoring, and setting engineering best practices.\nStrong Python understanding: idiomatic code, testing, packaging, async I/O, performance profiling.\nStrong ML, DL, and GenAI understanding: supervised/unsupervised learning, transformer architectures, prompt engineering, evaluation, and safety/guardrails.\nExperience building LLM-based workflows and agents, including retrieval (RAG), tools, memory, and evaluation loops.\nHands-on experience with LangChain, HuggingFace, PyTorch, Neo4j, Milvus, and AWS for scalable, production-grade systems.\nMLOps experience: model deployments, API development, observability, infra management (containers, orchestration, IaC), and data/version governance.\n\nPreferred Qualifications: \nKnowledge graph design and Neo4j schema modeling for semantic retrieval and reasoning.\nLLM fine-tuning with deployment to production: LoRA/QLoRA, PEFT, quantization, evaluation, rollback strategies, and cost optimization.\nStartup experience: comfort with ambiguity, 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