{"id":64712,"url":"https://alion.io/job/quantum-capital-group-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2789,"name":"Quantum Capital Group","domain":"quantumcap.com","url":"https://alion.io/company/quantum-capital-group","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Context Engineering","optional":false},{"name":"GCP","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-19T23:20:40Z","employer_posted_date":null,"last_verified_at":"2026-08-19T23:20:40Z","board_verified":false,"closed_at":null,"days_open":58,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":58},"description":"Responsibilities:\nDevelop and deploy agentic AI workflows for private equity value chains at Quantum.\nThis involves combining financially focused workflows with knowledge from the energy industry.\nImplement state-of-the-art practices in context engineering for JTBD-focused AI agents, including evals for agents.\nResearch, train and tune AI agents to optimise eval metrics for specific objectives, including research on evals for qualitative tasks.\nWork in tools, functions and MCP services to augment AI-ready data pipelines.\nDevelop/tune web services to scale model inferencing.\nIntegrate data from multiple structured and unstructured sources into unified data platforms.\nImplement best practices for data quality, governance, security, and compliance.\nWork with data scientists and software engineers to ensure seamless integration of data for modelling and application needs.\nOptimise performance of data and model pipelines and troubleshoot issues in real time.\nCollaborate with stakeholders to define data requirements and deliver reliable solutions.\nRequirements:\n7 years of experience in machine learning engineering or related roles.\nProficiency in using generative AI tools for accelerating software development and data workflows.\nHands-on experience in evaluating ML model metrics and tuning existing models.\nHands-on experience in developing web services (using any stack/framework).\nPreference for web services performing model inference.\nHands-on expertise with data pipeline frameworks (Apache Airflow, Spark, Kafka, etc. ).\nStrong programming skills in Python, SQL, Java, or Scala.\nExperience with cloud-based data platforms (AWS, Azure, or GCP).\nProficiency in using generative AI tools for accelerating software development and data workflows.\nKnowledge of API integration, data streaming, and real-time processing.\nStrong problem-solving, analytical, and debugging skills.\nA bachelor's or master's degree in computer science, data engineering, or a related 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