{"id":1285126,"url":"https://alion.io/job/inypeople-technology-data-scientist","title":"Data Scientist","company":{"id":3806427,"name":"Inypeople Technology","domain":"inypeople.com","url":"https://alion.io/company/inypeople-technology","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","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":21000,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":51},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Accelerate","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWQ","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"DPO","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"GGUF","optional":false},{"name":"GPTQ","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"RLHF","optional":false},{"name":"TGI","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false},{"name":"Triton","optional":false},{"name":"TRL","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-08-17T11:53:38Z","employer_posted_date":null,"last_verified_at":"2026-08-17T11:53:38Z","board_verified":false,"closed_at":null,"days_open":42,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":42},"description":"Job Specific Duties and Responsibilities :\n\n- End-to-end ML ownership : Drive the complete lifecycle data curation, model building, evaluation, deployment, monitoring, and retraining for both predictive and generative AI systems.\n\n- Production-grade MLOps : Build scalable pipelines for training, CI/CD, model registry, A/B testing, drift detection, and automated retraining. Optimize inference for latency, throughput, and cost.\n\n- LLMs and SLMs : Fine-tune and deploy open and closed models using techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference tuning (RLHF/DPO). Apply quantization and distillation where needed.\n\n- Agentic systems : Design and productionize agentic frameworks RAG pipelines, tool/function calling, memory, planning loops, and multi-agent orchestration with appropriate guardrails and observability.\n\n- Quality and trust : Build evaluation frameworks (offline + online, including LLM-as-judge and red-teaming). Diagnose and mitigate hallucinations, bias, and drift.\n\n- Rapid innovation : Track SOTA research, prototype quickly, and showcase work through demos and tech talks to internal stakeholders and leadership.\n\nRequired Qualifications :\n\n- 5+ years of hands-on experience as an AI/ML Engineer or Applied Scientist, with proven production deployments including at least one LLM-based or agentic system taken to production.\n\n- Strong Python skills and solid software engineering fundamentals (version control, testing, design patterns, code reviews).\n\n- Deep Learning & NLP : Strong grasp of transformer architectures, attention, tokenization, embeddings, and modern NLP techniques. Hands-on with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, TRL, Accelerate).\n\n- Agentic & RAG stack : Working knowledge of frameworks such as LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen, plus vector stores (Pinecone, Weaviate, Qdrant, pgvector, or FAISS) and reranking strategies.\n\n- Serving & optimization : Experience with inference servers such as vLLM, TGI, or Triton, and familiarity with quantization (GPTQ, AWQ, GGUF).\n\n- MLOps & infra : Hands-on with tools like MLflow, Weights & Biases, Airflow, or Kubeflow; comfortable with Docker, Kubernetes, GPU workloads, and at least one major cloud (AWS / Azure / GCP).\n\n- Soft skills : High bias for action, strong communication, ownership mindset, and intellectual curiosity.\n\nEducation :\n\n- B.Tech or M.Tech in Computer Science, Data Science Engineering, AI/ML Engineering, or a closely related quantitative discipline.\n\n- Equivalent practical experience supported by a strong portfolio (open-source work, publications, or production deployments) will also be considered.\n\nSoft Skills :\n\n- Strong problem-solving and ownership mindset; comfortable operating in ambiguity.\n\n- Clear communication of technical tradeoffs and experiment results to stakeholders.\n\n- Collaborative approach with engineering, product, and data teams.\n\nInterview Rounds :\n\n- L1 - Virtual Round\n\n- L2 - F2F\n\nLocation :\n\n- Bangalore\nSkills\nData Science, Data Scientist, Artificial Intelligence, LLM, RAG, Agentic AI, Deep Learning, Python, NLP, LangChain, Machine Learning","description_format":"text","description_chars":3154,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Staffing & Staff Augmentation"],"lifecycle":[{"event":"open","at":"2026-09-26T04:00:00Z"}],"liveness":{"score":15,"band":"cold","label":"Long shot","p_open":0.6,"p_active":0.569,"p_room":0.45,"age_days":41,"expected_fill_days":23,"reasons":["seen:41","win:tail"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/inypeople-technology-data-scientist","json_url":"https://alion.io/job/inypeople-technology-data-scientist.json","meta":{"generated_at":"2026-09-29T00:01:17Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4,"day_limit":5000,"remaining_today":4996,"minute_limit":60,"resets_at":"2026-09-30T00:00:00Z"}}}