{"id":3959676,"url":"https://alion.io/job/weekday-ai-data-engineer","title":"AI Data Engineer","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":100,"ghost_share":0,"stale_share":0.98,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","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":{"min":4500000,"max":6500000,"currency":"INR","period":"year","gross":null,"usd_annual":69999},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Redshift","optional":true},{"name":"Google BigQuery","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true}],"status":"live","first_seen_at":"2026-11-09T10:49:37Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T10:49:37Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"This role is for one of Weekday’s clients\nSalary range: Rs 4500000 - Rs 6500000 (ie INR 45 - 65 LPA)\nMin Experience: 7+ years\nLocation: Bengaluru\nJobType: full-time\nWe are looking for an experienced and technically proficient AI Data Engineer to design, develop, and manage scalable data infrastructure that powers advanced Artificial Intelligence (AI), Machine Learning (ML), and Generative AI applications. The ideal candidate will have strong expertise in data engineering, distributed computing, cloud platforms, and modern data architectures, along with an understanding of AI/ML workflows and data-intensive systems.\nYou will be responsible for building robust data pipelines, developing scalable data platforms, and ensuring the availability, quality, and reliability of structured and unstructured data for AI-driven applications. This role requires someone who can work across engineering, data science, and AI teams to translate complex business requirements into efficient data solutions.\nRequirements\nKey Responsibilities\n1. Data Engineering & Pipeline Development\nDesign, develop, and maintain scalable ETL/ELT pipelines for processing large volumes of structured and unstructured data.\nBuild reliable batch and real-time data processing systems to support AI/ML applications.\nDevelop data ingestion, transformation, integration, and orchestration frameworks.\nOptimize data workflows for performance, scalability, and cost efficiency.\n2. AI & Machine Learning Data Infrastructure\nBuild and maintain data infrastructure supporting machine learning, deep learning, and Generative AI applications.\nDevelop data pipelines for model training, validation, testing, and deployment.\nWork with vector databases, embeddings, and retrieval-augmented generation (RAG) architectures.\nPrepare, clean, transform, and validate datasets for AI model development.\nSupport feature engineering, feature stores, and model data management.\n3. Data Architecture & Cloud Infrastructure\nDesign scalable data architectures using modern data platforms and distributed computing frameworks.\nWork with cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform.\nImplement data lakes, data warehouses, and lakehouse architectures.\nLeverage technologies such as Apache Spark, Kafka, Airflow, Databricks, Snowflake, and Delta Lake.\n4. Data Quality, Governance & Security\nEstablish data quality checks, monitoring, validation, and observability frameworks.\nEnsure data consistency, integrity, security, and compliance.\nImplement access controls, metadata management, and data governance practices.\nIdentify and resolve data pipeline bottlenecks and infrastructure issues.\n5. Collaboration & Technical Leadership\nCollaborate with AI engineers, ML engineers, data scientists, and product teams.\nLead technical discussions, architecture decisions, and engineering best practices.\nMentor junior engineers and drive continuous improvements in data engineering processes.\nEvaluate emerging AI and data technologies to improve platform capabilities.\nRequired Skills & Qualifications\n7-18 years of experience in Data Engineering, AI Data Engineering, or related engineering roles.\nStrong proficiency in Python, SQL, and data processing frameworks.\nHands-on experience with Apache Spark, Kafka, Airflow, and distributed data systems.\nExperience building large-scale ETL/ELT pipelines and modern data architectures.\nStrong understanding of AI/ML data workflows and Generative AI infrastructure.\nExperience with cloud platforms (AWS, Azure, or GCP).\nKnowledge of vector databases, embeddings, RAG pipelines, and unstructured data processing.\nExperience with data warehouses, data lakes, and lakehouse platforms.\nStrong understanding of database technologies, data modelling, and optimization techniques.\nFamiliarity with Docker, Kubernetes, CI/CD, and infrastructure automation.\nGood-to-Have Skills\nExperience working with LLMs and AI-powered applications.\nExposure to LangChain, LlamaIndex, and model-serving infrastructure.\nKnowledge of MLOps, feature stores, and model lifecycle management.\nExperience with Databricks, Snowflake, BigQuery, or Redshift.\nFamiliarity with data observability and governance 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