{"id":3282532,"url":"https://alion.io/job/jsm-consulting-inc-ai-risk-engineer","title":"AI Risk Engineer","company":{"id":4443931,"name":"JSM Consulting Inc.","domain":"jsmconsulting.com","url":"https://alion.io/company/jsm-consulting-inc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Copilot","optional":true},{"name":"Databricks","optional":true},{"name":"EU AI Act","optional":true},{"name":"GDPR","optional":true},{"name":"Interpretability","optional":true},{"name":"Machine Learning","optional":true},{"name":"NIST AI RMF","optional":true},{"name":"Power Automate","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-26T17:09:31Z","employer_posted_date":null,"last_verified_at":"2026-10-26T17:09:31Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Shift Timings : 01:30-10:30PM\n\nMandatory 3 top Skills Mandatory 3 top Good to have Skills\n\n Experience with enterprise AI platforms (e.g., Azure ML / Azure AI Foundry, Databricks).\n Experience developing and evaluating machine learning models using Python and libraries such as scikit-learn, TensorFlow, or PyTorch.\n Familiarity with emerging AI regulations and industry frameworks (e.g., EU AI Act, NIST AI Risk Management Framework, IOGP Responsible Use of AI) 2. Experience with generative AI or agentic AI frameworks, tools, or applications, and with governing AI agents (e.g., agent registries, Microsoft Agents 365).\n Proficiency in Python and experience building automation, scripts, or data pipelines that integrate with enterprise platforms and APIs. 3. Familiarity with emerging AI regulations and industry frameworks (e.g., EU AI Act, NIST AI Risk Management Framework, IOGP Responsible Use of AI).\n Demonstrated ability to reason through ethical implications of AI systems and articulate trade-offs\n\nRole Designation: AI Risk Engineer\n\nOverview\n\nAI Risk Engineer on Chevron's Responsible AI (RAI) team partners with business and IT development teams to help ensure that AI and machine learning systems—including third-party tools, generative AI, and AI agents—are fair, transparent, accountable, secure, and aligned with Chevron's ethical principles. Working at the intersection of AI/ML engineering, ethics, and policy, you will build and maintain the automation, scripts, and tooling that power Chevron's AI impact assessment process, and help operationalize Responsible AI controls within our AI governance platform (Credo AI). You will collaborate closely with our Cybersecurity, Legal, and Data Privacy teams to ensure that AI solutions, vendors, and internal use cases adhere to Chevron guidelines, policies, and emerging regulations.\n\nResponsibilities for this position may include but are not limited to:\n\nBuild, maintain, and enhance the automation flows, scripts, and tooling that support Chevron's AI impact assessment and Responsible AI review processes.\n\nConfigure and administer Chevron's AI governance platform (Credo AI)—adding risk scenarios, controls, and review workflows that enable teams to document mitigations, provide evidence, and track AI risks across the lifecycle.\n\nDesign and operate review and approval checkpoints, including pre-production questionnaires and periodic re-reviews of AI use cases, streamlining through automation where possible.\n\nEstablish and run model review processes for both pre-trained/foundation models and custom-built machine learning models.\n\nAssist in AI Discovery and maintain an inventory of AI models, agents, and use cases across the enterprise, ensuring proper documentation and lifecycle governance.\n\nConduct ethical risk assessments and impact analyses for AI use cases, including alignment with regulations such as the EU AI Act and industry frameworks such as the IOGP Responsible Use of AI principles, and NIST AI Risk Management Framework.\n\nEvaluate AI vendors, models, and external AI access requests for fairness, security, and policy compliance, partnering with Cybersecurity, Legal, and Data Privacy.\n\nInfluence the development and adoption of tools and methodologies to detect and mitigate bias, drift, and unintended consequences in machine learning and generative AI systems.\n\nLead training, FAQs, and awareness campaigns—leveraging Teams and Copilot—to promote ethical AI practices and communicate process changes across business units.\n\nPartner with cross-functional teams to integrate Responsible AI principles into product development and deployment workflows.\n\nRequired Qualifications\n\nBachelor's degree in Computer Science, Data Science, Engineering, or a related field.\n\nMinimum 3-7 years of professional experience in data science, analytics, or AI/ML engineering roles.\n\nProficiency in Python and experience building automation, scripts, or data pipelines that integrate with enterprise platforms and APIs.\n\nDemonstrated ability to reason through ethical implications of AI systems and articulate trade-offs.\n\nExperience working with interdisciplinary teams (e.g., cybersecurity, legal, privacy, policy) to embed Responsible AI principles.\n\nStrong communication and presentation skills to convey complex technical concepts to diverse audiences.\n\nFamiliarity with data governance frameworks and enterprise compliance standards (e.g., GDPR, CCPA, NIST, OECD).\n\nPreferred Qualifications\n\nMaster's degree in Data Science, Computer Science, or a related field.\n\nCertifications in Responsible AI, Data/AI Ethics, Cybersecurity, or AI Governance.\n\nExperience developing and evaluating machine learning models using Python and libraries such as scikit-learn, TensorFlow, or PyTorch.\n\nExperience with generative AI or agentic AI frameworks, tools, or applications, and with governing AI agents (e.g., agent registries, Microsoft Agents 365).\n\nExperience with AI governance or model-risk platforms (e.g., Credo AI) and workflow automation (e.g., Power Automate, Azure DevOps).\n\nKnowledge of fairness, interpretability, and robustness techniques in AI.\n\nExperience with enterprise AI platforms (e.g., Azure ML / Azure AI Foundry, Databricks, DataRobot).\n\nFamiliarity with emerging AI regulations and industry frameworks (e.g., EU AI Act, NIST AI Risk Management Framework, IOGP Responsible Use of AI).\n\nProven ability to work autonomously in ambiguous environments and make connections between disparate 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