{"id":3203377,"url":"https://alion.io/job/sprouts-senior-data-engineer-aiml","title":"Senior Data Engineer - AI/ML","company":{"id":2984334,"name":"Sprouts","domain":"sprouts.ai","url":"https://alion.io/company/sprouts-ai","size_band":"11-50","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":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Chandigarh, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"Amazon S3","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"ElasticSearch","optional":true},{"name":"Kubernetes","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Trino","optional":true}],"status":"live","first_seen_at":"2026-10-24T08:43:02Z","employer_posted_date":null,"last_verified_at":"2026-10-24T08:43:02Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Senior Data Engineer : \n\nExperience : 5+ years.\n\nLocation : Bangalore / Chandigarh (Hybrid).\n\nRemote possible for the right person.\n\nType : Full-Time.\n\nReports to : Chief Architect.\n\nAbout us : \n\nWe are building an AI-native GTM intelligence platform for enterprise revenue teams.\n\nOur data is the product.\n\nWe hold 45M+ companies and hundreds of millions of contacts, built from many vendor feeds and served into search and AI features.\n\nData quality is our highest priority.\n\nEvery AI feature we ship is only as good as the data underneath it.\n\nSo the data engineer is a central role here, not a supporting one.\n\nThe role : \n\nWe are looking for a strong data engineer who can do two things well.\n\n- First, the traditional craft.\n\n- Build reliable pipelines that keep large-scale data correct and fresh.\n\nThis is a must.\n\n- Everything else sits on top of it.\n\nSecond, the new craft.\n\n- Use AI to process and clean data at a scale and quality that rules and scripts alone cannot reach.\n\n- You will own data as a product, from raw vendor feeds to the clean records the whole platform runs on.\n\nWhat you'll own : \n\n- Data as a product : Own the quality, freshness, and correctness of the data you ship.\n\n- Treat the teams and features that consume it as your customers.\n\n- Care that the data is right, not just that the job ran.\n\n- Pipelines at scale : Build and run incremental pipelines that turn messy vendor feeds into clean, trustworthy, queryable data across hundreds of millions of records.\n\n- AI for data processing : Use LLMs and smaller models to parse, classify, extract, and enrich data at scale, and do it cost-effectively.\n\n- AI for data cleaning : Use AI to normalize, deduplicate, and resolve identity, and to catch bad data that rules miss.\n\n- Match and merge the same company or person across many vendors into one record, and resolve conflicts between sources.\n\n- Correctness you can prove : Build the checks, gates, and observability that catch bad data before it reaches a customer.\n\n- Be able to show the data is right.\n\n- An AI-native way of working : Use AI tools every day to design, build, and ship faster.\n\nWhat we're looking for : \n\n- 5+ years building production data pipelines.\n\n- Strong distributed data processing (Spark or similar) and strong SQL and data modeling.\n\n- Experience with incremental or CDC pipelines, not just full batch reloads.\n\n- The ability to keep large-scale data correct, fresh, and performant.\n\n- You read unfamiliar code and ship without hand-holding.\n\n- Good engineering judgment and clear communication.\n\nWhat sets you apart (AI-native) : \n\n- You use AI tools daily in your own engineering work.\n\n- You have used AI, LLMs, or ML to process, clean, or resolve data, or you are excited to.\n\n- Not just to write code, but on the data itself.\n\n- You think about data as a product, with consumers, contracts, and quality that is measured.\n\n- You separate 'the pipeline succeeded' from 'the data is correct,' and you care about the second one.\n\nOur stack : Python, PySpark, Apache Iceberg, Project Nessie, Trino, Airflow, Elasticsearch, Postgres, S3, Kubernetes.\n\nWhy join us : \n\n- Data is the core of the product, not a side system.\n\n- You will help build an AI-native data platform, not just maintain pipelines.\n\n- Hard, real problems : incremental loads at scale, entity resolution, and AI-driven cleaning across hundreds of millions of records.\n\n- High ownership and direct impact on what ships.\n\n- A small, strong team and a fast pace.\n\nSkills\nData Engineering, Python, PySpark, Data Modeling, Machine Learning, Generative AI, Artificial Intelligence","description_format":"text","description_chars":3605,"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":["Artificial Intelligence","Sales & Marketing","Marketing Automation"],"lifecycle":[{"event":"open","at":"2026-10-24T10:30:05Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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