{"id":3230853,"url":"https://alion.io/job/cai-data-scientist-agentic-ai-developer","title":"Data Scientist - Agentic AI Developer","company":{"id":1819291,"name":"CAI","domain":"cai.io","url":"https://alion.io/company/cai-io","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":25,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"IAM","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LightGBM","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 Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"OCR","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pandas","optional":false},{"name":"PEFT","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"XGBoost","optional":false},{"name":"Kubernetes","optional":true},{"name":"Spark","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-10-23T00:00:00Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-29T03:43:12Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Data Scientist - Agentic AI DeveloperReq number:\nR8813Employment type:\nFull timeWorksite flexibility:\nRemoteWho we are\nCAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right-whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.\nJob Summary\nWe are looking for a motivated Data Scientist - Agentic AI Developer ready to take us to the next level! If you have advanced Python, Databricks, and AWS Bedrock experience and are looking for your next career move, apply nowJob Description\nWe are looking for a Data Scientist - Agentic AI Developer to develop, deploy, and monitor production-ready machine learning, Generative AI, LLM, RAG, and Agentic AI solutions. This position will be full-time and remote.\nWhat You’ll Do\nPartner with business and technical stakeholders to define problems, success metrics, KPIs, constraints, and solution approaches\nPerform data sourcing, exploratory data analysis, statistical analysis, feature engineering, data-quality checks, and hypothesis testing\nBuild and evaluate machine learning, deep learning, time-series, anomaly-detection, recommendation, and optimization models\nDevelop production-ready LLM, RAG, and Agentic AI applications using LangGraph, LangChain, Amazon Bedrock Agents, or similar frameworks\nDesign multi-step and multi-agent workflows involving tool calling, state and memory management, human-in-the-loop approvals, retries, fallbacks, tracing, and MCP integrations\nBuild secure and scalable AI solutions using Databricks Apps, MLflow, Model Serving, Vector Search, Workflows, Delta Lake, and Unity Catalog\nDevelop applications on Amazon Bedrock using foundation models, Knowledge Bases, Agents, Guardrails, prompt management, and model evaluation\nDesign advanced RAG pipelines involving hybrid retrieval, metadata filtering, re-ranking, query rewriting, embeddings, vector databases, and retrieval evaluation\nBuild NLP solutions for text classification, named entity recognition, information extraction, semantic search, and document summarization\nDevelop computer vision solutions for classification, object detection, segmentation, OCR, and visual defect inspection\nFine-tune LLMs, embedding models, and vision models using LoRA, QLoRA, PEFT, and Hugging Face frameworks\nDevelop and deploy AI services using FastAPI, REST APIs, Docker, Lambda, API Gateway, ECS, EKS, and CI/CD pipelines\nMonitor model performance, drift, quality, latency, reliability, and cost\nConduct error analysis, model evaluation, and continuous improvement based on technical and business metrics\nCommunicate technical findings, trade-offs, risks, and recommendations to business and engineering stakeholders\nWhat You’ll Need\nRequired:\n6 to 8 years of Data Science experience, including at least 3 years of hands-on Generative AI experience\nAt least 5 years of advanced Python development experience\nStrong expertise in Python, Pandas, NumPy, PySpark, SQL, and object-oriented programming\nStrong foundations in statistics, probability, machine learning, deep learning, hypothesis testing, and model evaluation\nHands-on experience with Scikit-learn, XGBoost, LightGBM, and PyTorch or TensorFlow\nExperience building models and AI applications from first principles rather than relying only on AutoML or pre-built components\nHands-on experience building LLM applications using Claude through Amazon Bedrock or other foundation models\nStrong understanding of prompt engineering, structured outputs, function and tool calling, context management, caching, and LLM evaluation\nProven experience building at least one end-to-end multi-step or multi-agent system\nExperience with agent tool design, state and memory management, human-in-the-loop workflows, retries, fallbacks, evaluation, tracing, and MCP\nExperience with RAG, embeddings, vector databases, hybrid search, metadata filtering, re-ranking, and query rewriting\nKnowledge of retrieval evaluation metrics including recall@k, MRR, and nDCG\nHands-on experience with Databricks Apps, MLflow, Model Serving, Vector Search, Workflows, Delta Lake, and Unity Catalog\nHands-on experience with AWS Bedrock, S3, IAM, Lambda, API Gateway, OpenSearch, SageMaker, and CloudWatch\nExperience with FastAPI, REST APIs, Git, Docker, automated testing, CI/CD, and Infrastructure as Code\nExperience deploying and monitoring production-ready machine learning and Generative AI solutions\nStrong stakeholder communication skills and the ability to explain technical trade-offs to business and engineering teams\nPreferred:\nExperience developing NLP solutions including text classification, named entity recognition, information extraction, semantic search, and document summarization\nExperience developing computer vision solutions including classification, object detection, segmentation, OCR, and visual defect inspection\nExperience fine-tuning LLMs, embedding models, or vision models using LoRA, QLoRA, PEFT, or Hugging Face\nExperience deploying containerized AI services using ECS or EKS\nExperience working on enterprise-scale AI solutions in manufacturing, industrial, engineering, or supply chain environments\nPhysical Demands\nAbility to safely and successfully perform the essential job functions\nSedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.\nAbility to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor\nReasonable accommodation statement\nIf you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise 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