{"id":3027744,"url":"https://alion.io/job/exl-agentic-ai-data-engineer","title":"Agentic AI Data Engineer","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":80,"open_postings":252,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-24T06:02:35Z"}},"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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLMOps","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-07-30T09:50:23Z","employer_posted_date":"2026-07-30","last_verified_at":"2026-10-24T11:27:04Z","board_verified":true,"closed_at":null,"days_open":86,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":86},"description":"Agentic AI Data Engineer\nRole Overview\nTotal Experience required : 5-10 Years\nWe are seeking a highly skilled Agentic AI Data Engineer to design, build, and optimize intelligent, autonomous data systems that power next-generation AI applications. This role blends data engineering, machine learning infrastructure, and emerging agent-based AI frameworks to enable scalable, self-orchestrating pipelines and decision-making systems.\nYou will work at the intersection of data platforms, large language models (LLMs), and cloud-native architectures-building systems that can reason, act, and adapt autonomously.\nKey Responsibilities\nDesign and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines\nBuild scalable data pipelines for structured and unstructured data (batch + real-time)\nDevelop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks\nIntegrate AWS AI/ML services into production-grade architectures\nDevelop and optimize data lakes, warehouses, and lakehouse architectures\nBuild APIs and microservices to expose AI/ML capabilities\nEnsure data quality, governance, and security across pipelines\nCollaborate with data scientists, ML engineers, and product teams to deploy AI solutions\nImplement monitoring, logging, and observability for AI agents and pipelines\nOptimize cost and performance of cloud-based AI workloads\nRequired Technical Skills\nCloud & AWS Ecosystem\nStrong experience with AWS services, including:Amazon S3, Glue, Lambda, Step Functions\nAmazon Redshift / Athena\nAmazon SageMaker (training, deployment, pipelines)\nAmazon Bedrock (foundation models, agents, knowledge bases)\n\nAI/ML & Agentic Systems\nExperience with LLMs and generative AI systems\nHands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)\nFamiliarity with AgentCore / agent orchestration platforms\nUnderstanding of RAG architectures, embeddings, and vector databases\nExperience with model deployment, inference optimization, and prompt engineering\nData Engineering\nStrong proficiency in Python and SQL\nExperience with ETL/ELT tools and frameworks\nDistributed data processing (Spark, PySpark, or similar)\nStreaming technologies (Kafka, Kinesis, or similar)\nData modeling and schema design\nData & AI Infrastructure\nExperience with vector databases (e.g., Pinecone, FAISS, OpenSearch)\nKnowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)\nContainerization (Docker) and orchestration (Kubernetes)\nCI/CD for ML and data pipelines\nPreferred Qualifications\nExperience building autonomous AI agents for enterprise use cases\nKnowledge of multi-agent collaboration systems and planning algorithms\nFamiliarity with LangChain, LlamaIndex, or similar frameworks\nExperience with MLOps and LLMOps practices\nUnderstanding of graph-based workflows and knowledge graphs\nExposure to real-time AI systems and event-driven architectures\nSoft Skills\nStrong problem-solving and system design skills\nAbility to work in fast-paced, evolving AI environments\nEffective communication and cross-functional collaboration\nCuriosity and adaptability to emerging AI technologies\nEducation & Experience\nBachelor’s or Master’s degree in Computer Science, Engineering, or related field\n4+ years of experience in data engineering or ML engineering\nHands-on experience with production-grade AI/ML systems\nNice-to-Have\nExperience with reinforcement learning or planning systems\nBackground in distributed systems design\nContributions to open-source AI/data projects\nCertifications in AWS (e.g., Solutions Architect, Machine Learning Specialty)\nWhat You’ll Build\nAutonomous data pipelines that self-heal and optimize\nAI agents capable of reasoning over enterprise data\nScalable LLM-powered applications integrated with business workflows\nIntelligent systems that move beyond automation into decision-making\n Key Responsibilities\nDesign and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines\nBuild scalable data pipelines for structured and unstructured data (batch + real-time)\nDevelop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks\nIntegrate AWS AI/ML services into production-grade architectures\nDevelop and optimize data lakes, warehouses, and lakehouse architectures\nBuild APIs and microservices to expose AI/ML capabilities\nEnsure data quality, governance, and security across pipelines\nCollaborate with data scientists, ML engineers, and product teams to deploy AI solutions\nImplement monitoring, logging, and observability for AI agents and pipelines\nOptimize cost and performance of cloud-based AI workloads\n Required Technical Skills\nCloud & AWS Ecosystem\nStrong experience with AWS services, including:Amazon S3, Glue, Lambda, Step Functions\nAmazon Redshift / Athena\nAmazon SageMaker (training, deployment, pipelines)\nAmazon Bedrock (foundation models, agents, knowledge bases)\n\nAI/ML & Agentic Systems\nExperience with LLMs and generative AI systems\nHands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)\nFamiliarity with AgentCore / agent orchestration platforms\nUnderstanding of RAG architectures, embeddings, and vector databases\nExperience with model deployment, inference optimization, and prompt engineering\nData Engineering\nStrong proficiency in Python and SQL\nExperience with ETL/ELT tools and frameworks\nDistributed data processing (Spark, PySpark, or similar)\nStreaming technologies (Kafka, Kinesis, or similar)\nData modeling and schema design\nData & AI Infrastructure\nExperience with vector databases (e.g., Pinecone, FAISS, OpenSearch)\nKnowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)\nContainerization (Docker) and orchestration (Kubernetes)\nCI/CD for ML and data pipelines\nPreferred Qualifications\nExperience building autonomous AI agents for enterprise use cases\nKnowledge of multi-agent collaboration systems and planning algorithms\nFamiliarity with LangChain, LlamaIndex, or similar frameworks\nExperience with MLOps and LLMOps practices\nUnderstanding of graph-based workflows and knowledge graphs\nExposure to real-time AI systems and event-driven architectures","description_format":"text","description_chars":6274,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Business Process Outsourcing (BPO)","AI Consulting & Integration","Analytics & BI Consulting"],"lifecycle":[{"event":"open","at":"2026-10-21T19:33:11Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long 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