{"id":1227484,"url":"https://alion.io/job/cantik-technologies-ai-engineer-agentic-ai-knowledge-systems","title":"AI Engineer - Agentic AI & Knowledge Systems","company":{"id":3800693,"name":"Cantik Technologies","domain":"cantiktech.com","url":"https://alion.io/company/cantik-technologies","size_band":null,"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":"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":23000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Playwright","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Docker","optional":true},{"name":"Haystack","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LanceDB","optional":true},{"name":"LangChain","optional":true},{"name":"Langfuse","optional":true},{"name":"LangGraph","optional":true},{"name":"OpenAI SDK","optional":true},{"name":"SGLang","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-24T07:22:06Z","employer_posted_date":null,"last_verified_at":"2026-09-24T07:22:06Z","board_verified":false,"closed_at":null,"days_open":23,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":23},"description":"AI Engineer Agentic AI & Knowledge Systems : \n\nJoin our AI team to build and scale Agentic AI solutions, AI Assistants, and Workflow Automation platforms powered by Large Language Models (LLMs). You will work across the full AI lifecycle - from development and experimentation to deployment, monitoring, and optimization. The role sits at the intersection of Data Science, Software Engineering, and Cloud Engineering.\n\nKey Responsibilities : \n\n- Design and develop AI assistants and agentic workflow solutions.\n\n- Build and optimize RAG (Retrieval-Augmented Generation) applications.\n\n- Develop knowledge ingestion, extraction, and retrieval pipelines from documents, APIs, databases, and web sources.\n\n- Implement chunking strategies, semantic search, vector search, and graph-based retrieval.\n\n- Develop scalable AI services, APIs, and automation workflows.\n\n- Build browser automation solutions using Playwright and Puppeteer.\n\n- Deploy, monitor, and optimize containerized AI applications.\n\n- Contribute to AI testing, evaluation, benchmarking, and experimentation practices.\n\n- Apply software engineering best practices : testing, CI/CD, observability, and operational excellence.\n\nCore Skills : \n\n- Programming : Strong Python development skills.\n\n- AI Engineering : Experience building AI, ML, or data-driven solutions.\n\n- Generative AI : Experience with LLM-based applications and Generative AI solutions.\n\n- RAG & Retrieval : Hands-on experience with RAG, embeddings, vector databases, semantic search, and chunking strategies.\n\n- Data Processing : Understanding of web scraping and unstructured data processing.\n\n- Software Engineering : Experience with Git, testing frameworks, and CI/CD pipelines.\n\n- Cloud : Good understanding of Azure and cloud-native architectures.\n\n- MLOps / LLMOps : Familiarity with deployment, monitoring, and operational workflows for AI solutions.\n\n- AI Evaluation : Experience with AI testing, model evaluation, benchmarking, and performance optimization.\n\n- Browser Automation : Experience with Playwright or Puppeteer.\n\nTechnology Stack : \n\n- AI & Agent Frameworks : LangGraph, Haystack, OpenAI SDK\n\n- Observability & Eval : Langfuse\n\n- Knowledge & Retrieval : LanceDB, FalkorDB\n\n- Automation : Playwright, Puppeteer\n\n- Model Serving & Inf. : vLLM, SGLang\n\n- Platform & Infra : Azure Container Apps, Azure Container Registry (ACR), Docker\n\n- Development : Python, Ray\n\nSkills\nPython, Generative AI, RAG, LangGraph, Azure, Agentic AI, Knowledge Graph, Artificial Intelligence, Data Science, LLM","description_format":"text","description_chars":2534,"description_truncated":false,"requirements":{"experience_years_min":6,"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":["Artificial 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