{"id":3617797,"url":"https://alion.io/job/hcltech-ai-solution-architect-i-11","title":"AI Solution Architect I","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"LIME","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SHAP","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false}],"status":"live","first_seen_at":"2026-11-02T17:09:23Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-08T05:32:14Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Job Summary\nAI Engineer for Bias Assessments\nThe AI Engineer for Bias Assessments serves as HCLTech’s central technical specialist for conducting bias and fairness assessments, investigating potential root causes, and recommending and validating mitigation measures. The role combines hands-on statistical analysis and AI engineering with practical knowledge of responsible AI, algorithmic auditing, explainability, and data governance.\nThe engineer assesses HCLTech-developed and third-party AI systems used in HCLTech’s internal operations, including models, AI agents, decision-support tools, and AI-enabled workflows. The role identifies potential unfair impacts and disparate performance across relevant population groups and produces documented, reproducible evidence to support governance decisions.\nThe engineer works alongside LoB testers, engineering teams, and data scientists, providing specialist methods, tools, training, and technical review. LoB teams retain responsibility for their assigned testing and implementation activities.\nKey Responsibilities\nBias and Fairness Assessments\nPlan and conduct assessments across the AI lifecycle, including before deployment, after material changes, periodically during operation, and following relevant incidents.\nDefine assessment scope, intended use, affected populations, potential harms, data requirements, metrics, and acceptance criteria with system owners and relevant stakeholders.\nAssess classification, regression, ranking, recommendation, and decision-support systems; adapt evaluation methods for generative AI and agentic workflows.\nSelect, calculate, validate, and interpret appropriate measures, including selection and scoring rates, impact ratios, demographic parity, equal opportunity, equalized odds, error-rate disparities, calibration, and intersectional performance.\nEvaluate statistical uncertainty, confidence intervals, effect sizes, sample sufficiency, and practical significance. Identify when available evidence is insufficient to support a conclusion.\nDevelop reusable assessment scripts, notebooks, test suites, and reporting templates using Python and appropriate fairness and statistical tools.\nData Evaluation and Root-Cause Investigation\nDetermine whether training, validation, test, benchmark, production, and reference datasets are suitable for each assessment.\nEvaluate data provenance, lineage, representativeness, missingness, imbalance, labeling quality, measurement error, data leakage, and potential proxy variables.\nIdentify relevant protected characteristics and intersectional groups, working with Legal and Privacy to confirm appropriate collection, access, use, and handling of sensitive data.\nInvestigate potential sources of disparities across data collection, labeling, feature transformations, model objectives, thresholds, prompts, retrieval sources, agent interactions, and human decision processes.\nUse subgroup analysis, error analysis, controlled comparisons, sensitivity analysis, and explainability techniques such as SHAP or LIME to investigate potential causes. Recognize that feature attribution alone does not establish causation or demonstrate fairness.\nGenerate or evaluate synthetic data where appropriate for scenario testing or underrepresented cases, documenting its assumptions, limitations, and suitability.\nMitigation and Validation\nRecommend practical mitigation options, including data remediation, feature or model changes, fairness constraints, threshold adjustments, prompt or retrieval improvements, human review, workflow controls, monitoring, and use restrictions.\nExplain trade-offs involving fairness, accuracy, privacy, reliability, and operational performance.\nWork with accountable technical and business teams to establish remediation plans with owners, actions, milestones, target dates, acceptance criteria, and validation methods.\nReassess systems after remediation to determine whether disparities have improved and identify residual risks or unintended effects. Recommend ongoing monitoring for changes in data, system behavior, users, and affected populations.\nEscalate material, unexplained, or recurring disparities and overdue corrective actions through HCLTech’s AI governance process.\nLoB Enablement and Audit Support\nServe as the central technical expert supporting LoB testers through assessment planning, troubleshooting, peer review, and interpretation of findings.\nDevelop practical guidance and train internal teams to conduct consistent, reproducible assessments.\nPrepare assessment reports, stakeholder summaries, and audit evidence covering scope, datasets, methodology, results, limitations, root-cause findings, remediation, and residual risks.\nExplain findings clearly to Engineering, Data Science, business owners, governance, Legal, Privacy, Risk & Compliance, and leadership.\nSkill Requirements\nDemonstrated hands-on experience independently conducting bias and fairness assessments, with the ability to explain dataset selection, metric rationale, statistical limitations, root-cause investigation, mitigation options, and validation results.\n\nStrong Python and SQL proficiency, including practical experience with pandas, NumPy, scikit-learn, statistical libraries, and reproducible analytical workflows.\n\nHands-on experience with AI Fairness 360, Fairlearn, or equivalent tools, with the ability to explain and validatethe underlying calculations.\n\nStrong understanding of fairness metric families, their assumptions and limitations, and trade-offs between different fairness objectives.\n\nStrong statistical skills, including sampling, confidence intervals, hypothesis testing, effect sizes, small-sample limitations, and intersectional analysis.\n\nExperience selecting appropriate datasets, identifyingdata-quality problems and proxy variables, and investigating potential sources of bias.\n\nWorking knowledge of explainability techniques such as SHAP and LIME and their limitations.\n\nAbility to translate assessment findings into implementable technical and operational improvements.\n\nExperience producing evidence suitable for governance reviews or external audit scrutiny and explaining methodologyto reviewers.\n\nAbility to advise and train internal testers and coordinate work across technical and governance teams.\n\nWorking knowledge of protected characteristics, discrimination risks, responsible AI, data protection, and AI governance, with the judgment to seekjurisdiction-specific advice.\n\nClear written and verbal communication, sound technical judgment, attention to detail, and willingness to escalate difficult findings.\n\nDegree in computer science, data science, statistics, mathematics, engineering, or a relateddiscipline, or equivalent practical experience.\n\nOther 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