{"id":3776486,"url":"https://alion.io/job/aventra-ai-data-engineer-ai-engineer","title":"Data Engineer / AI Engineer","company":{"id":4443918,"name":"aventra.ai","domain":"aventra.ai","url":"https://alion.io/company/aventra-ai","size_band":null,"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":"full_time","work_mode":"hybrid","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":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Chroma","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Hadoop","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"PostgreSQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-04T17:19:36Z","employer_posted_date":null,"last_verified_at":"2026-11-04T17:19:36Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Job Description – Senior Data Engineer / AI Engineer\nExperience: 8+ Years\nLocations: Bengaluru & Pune\nWork Mode: Hybrid (Work From Office)\nRole Type: Full-Time\nJob Summary\nWe are looking for an experienced Senior Data Engineer / AI Engineer with strong expertise in Big Data technologies, data engineering, distributed computing, and AI/ML engineering.\nThe ideal candidate should have hands-on experience designing and developing scalable data pipelines, processing large volumes of structured and unstructured data, and building AI-powered data solutions. The candidate should possess strong programming skills, experience with modern Big Data ecosystems, and the ability to integrate AI/ML capabilities into enterprise data platforms.\nKey Roles & Responsibilities\n1. Data Engineering & Big Data Development\nDesign, develop, and maintain scalable and high-performance data pipelines.\nBuild robust ETL/ELT workflows for processing structured, semi-structured, and unstructured data.\nWork with large-scale distributed data processing frameworks.\nDevelop data ingestion, transformation, validation, and orchestration workflows.\nOptimize data pipelines for performance, scalability, reliability, and cost efficiency.\nImplement batch and real-time data processing solutions.\n2. Big Data Technologies\nWork extensively with Apache Spark, PySpark, Hadoop, Hive, and Kafka.\nDevelop distributed data processing applications using Spark and PySpark.\nImplement real-time streaming solutions using Kafka or similar technologies.\nWork with data lakes, data warehouses, and lakehouse architectures.\nOptimize SQL queries, data models, and large-scale data processing jobs.\nExperience with Delta Lake, Apache Iceberg, or Hudi is an added advantage.\n3. AI / Machine Learning Engineering\nDesign and develop AI/ML solutions integrated with enterprise data platforms.\nBuild and deploy machine learning models and data-driven applications.\nWork with Python-based AI/ML frameworks and libraries.\nDevelop AI-powered workflows using LLMs, Generative AI, and related technologies.\nIntegrate LLM APIs and AI capabilities into existing data pipelines and enterprise applications.\nWork on RAG-based solutions, embeddings, vector databases, and semantic search.\nImplement model evaluation, monitoring, and performance optimization.\n4. Cloud & Data Platform Technologies\nHands-on experience with cloud-based data platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).\nExperience with services such as AWS S3, Glue, EMR, Redshift, Azure Data Factory, Azure Databricks, or Google BigQuery.\nBuild and manage scalable cloud-native data engineering solutions.\nImplement data storage, processing, and analytics solutions across cloud environments.\n5. Data Architecture & Governance\nDesign scalable data architectures, data models, and data integration frameworks.\nImplement data quality checks, data validation, and data governance practices.\nEnsure data security, privacy, and compliance across data platforms.\nWork closely with data scientists, AI engineers, architects, and business stakeholders.\nRequired Technical Skills\nSkill CategoryRequired Skills\nProgramming\nPython, PySpark, SQL\nBig Data\nApache Spark, Hadoop, Hive\nStreaming\nApache Kafka, Spark Streaming\nData Engineering\nETL/ELT, Data Pipelines, Data Warehousing\nData Architecture\nData Lake, Lakehouse, Delta Lake\nAI/ML\nMachine Learning, Generative AI, LLMs\nAI Frameworks\nLangChain, LlamaIndex, Scikit-learn (preferred)\nVector Databases\nPinecone, FAISS, Chroma, Milvus\nCloud\nAWS / Azure / GCP\nOrchestration\nApache Airflow, Azure Data Factory\nDatabases\nSQL, NoSQL, PostgreSQL, MongoDB\nMandatory Requirements\n8+ years of overall experience in Data Engineering, Big Data Engineering, or AI Engineering.\nStrong hands-on experience with Python, PySpark, and SQL.\nExtensive experience working with Big Data technologies such as Spark, Hadoop, Kafka, and Hive.\nStrong understanding of distributed computing and large-scale data processing.\nExperience developing and optimizing complex ETL/ELT data pipelines.\nGood understanding of AI/ML concepts and their integration with data engineering solutions.\nExperience working with cloud-based data platforms.\nStrong analytical, problem-solving, and communication 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