{"id":3712993,"url":"https://alion.io/job/symphoni-hr-data-and-ai-engineer","title":"Data and AI Engineer","company":{"id":3800108,"name":"Symphoni HR","domain":"symphonihr.com","url":"https://alion.io/company/symphoni-hr","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":93,"open_postings":41,"ghost_share":0,"stale_share":0,"repost_share":0.488,"time_to_fill_p50_days":45,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Hadoop","optional":false},{"name":"IAM","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Looker","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-11-03T23:56:32Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-04T16:49:11Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role sits at the intersection of legacy big data infrastructure and a modern AI-ready cloud ecosystem. You will keep existing data pipelines reliable while re-architecting them onto AWS and Databricks. You will also build and deploy intelligent AI agents and LLM-powered workflows that deliver immediate business value.\nOur client is a leading global technology and analytics company operating in the computer software industry. They serve large enterprises worldwide with data-driven solutions and are investing heavily in modern cloud platforms and generative AI capabilities. The organization is known for a collaborative, innovation-led culture that values engineering excellence.\nResponsibilities\nRe-architect legacy Apache Spark and Hadoop MapReduce workflows into optimized, production-ready pipelines on AWS and Databricks.\nDesign, build, and deploy AI agents and workflow automation tools using leading Large Language Models such as Anthropic Claude.\nBuild AI-oriented data pipelines covering unstructured data ingestion, feature tokenization, and metadata tagging for vector databases.\nMonitor, maintain, and troubleshoot existing Hadoop big data workloads, resolving bottlenecks and out-of-memory errors during migration.\nAct as the engineering liaison for BI stakeholders, resolving dashboard latency and optimizing data extracts for tools such as Tableau, Looker, and Power BI.\nUse Databricks and Delta Lake features to improve pipeline performance, reliability, and cost efficiency.\nLeverage Databricks AI tools such as Vector Search, Mosaic AI, and Lakeflow to deliver production-grade Generative AI and RAG applications.\nRequirements\n5 to 8 years of professional data engineering or software engineering experience in production environments.\nHands-on experience building AI agents, integrating LLM APIs (especially Anthropic Claude), and using orchestration frameworks such as LangChain or Databricks Mosaic AI.\nSolid hands-on experience with Apache Spark and a foundational understanding of partitioning, shuffling, caching, and broadcast joins.\nStrong proficiency in Python (PySpark) or Scala and intermediate-to-advanced SQL, including window functions, query tuning, and complex joins.\nPractical exposure to the AWS ecosystem (S3, IAM) and Databricks environments.\nExperience with at least one BI tool such as Tableau, Looker, or Power BI, including debugging calculated fields and troubleshooting slow reports.\nBachelor's degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field, or equivalent practical experience.\nSkills\nApache Spark, PySpark, Python, SQL, AWS (S3, IAM), Databricks, Hadoop (HDFS, MapReduce, Hive), LLM integration (Anthropic Claude), LangChain, Scala, Airflow, Control-M, Delta Lake, Unity Catalog, Vector Search, Mosaic AI, AWS Glue, AWS EMR, Tableau, Looker, Power BI, Copilot, Lakeflow","description_format":"text","description_chars":2877,"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":[],"lifecycle":[{"event":"open","at":"2026-11-03T23:56:32Z"}],"visa":[],"liveness":{"score":54,"band":"ok","label":"Likely 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