Senior AI Consultant

Job Description – Senior AI Consultant (Contract / Consultant)We are seeking a highly skilled Senior AI Consultant (Contract/Consultant) to oversee, evaluate, and guide our development teams in the successful execution of AI-driven projects. This role requires end-to-end ownership of solution quality, technical evaluation, and delivery alignment. The consultant must ensure that all AI solutions are architecturally sound, feasible, scalable, and aligned with business goals.Engagement ModelContract / Consultant RoleMinimum 1 day per week physical presence at office (for architecture reviews, team sync-ups, stakeholder meetings, audits).Virtual availability during weekdays for guidance, evaluations, and on-demand consultations.Full ownership of deliverable quality and final approvals for all AI components and architectures.Key Responsibilities1. Technical Evaluation Solution ArchitectureEvaluate proposed AI/ML use cases for feasibility, scalability, cost, and performance.Design end-to-end AI architecture: data ingestion, pipelines, model training, inference, deployment, and monitoring.Review models, prompts, LLM workflows, RAG pipelines, and data engineering processes.Identify risks, gaps, and remediation steps in ongoing AI implementations.2. Delivery Oversight GuidanceProvide continuous technical supervision to development teams.Conduct weekly/bi-weekly reviews (physical or virtual) of models, code, datasets, and workflows.Ensure compliance with AI/ML best practices, MLOps standards, and data security.Troubleshoot complex AI issues and guide teams toward resolution.Validate and sign-off on major releases, deployments, and production rollouts.3. AI Strategy, Planning Vendor EvaluationAssist in shaping organizational AI strategy and roadmap.Evaluate third-party AI vendors, tools, and technologies; provide recommendations.Suggest scalable Private/Public cloud frameworks (AWS/Azure/GCP) for implementation.Conduct POCs and benchmarking exercises when required.4. Documentation ReportingPrepare architecture diagrams, audit reports, model evaluation summaries, and risk assessments.Provide concise weekly reports on progress, blockers, and upcoming priorities.Present findings to leadership and project management teams.5. Mentorship Capability BuildingTrain and mentor internal developers on LLMs, generative AI, prompt engineering, ML pipelines, etc.Conduct workshops or tech sessions during on-site visits.Build internal AI/ML guidelines for long-term adoption.Key Result Areas (KRAs)1. Technical Quality Architecture GovernanceEnsuring AI architectures meet performance, scalability, and compliance requirements.Conducting structured evaluations of ML and generative AI components.2. Delivery Oversight Team AlignmentEnabling development teams to meet timelines with correct technical direction.Ensuring each AI component is reviewed, signed-off, and production-ready.3. Model Performance Feasibility ValidationEnsuring models perform as per expected KPIs and optimizing where required.Identifying risks early and mitigating them with clear action plans.4. Documentation, Reporting CommunicationProducing high-quality technical documentation and weekly progress reports.Clear communication with stakeholders and leadership.5. Knowledge Transfer Team EnablementStreamlining internal capabilities via training sessions, code reviews, and mentoring.Key Performance Indicators (KPIs)Architecture Technical Quality100% completion of architecture reviews before development starts.Zero critical technical gaps in AI solution designs (post-signoff).Reduction in rework due to early risk identification (target: Project Delivery TimelinesOn-time delivery of AI milestones (target adherence: 95%).Timely resolution of technical blockers (within agreed SLAs).Successful deployment signoffs without major revisions or rollbacks.Model System PerformanceAchievement of pre-defined model KPIs such as:Accuracy / Precision / Recall thresholdsLatency and throughput benchmarksRAG retrieval accuracy / prompt response qualityOptimized cost-performance ratio on cloud AI services.Stakeholder Engagement AvailabilityPhysical office presence at least 1 day/week (attendance compliance: 100%).Virtual availability for consultative support within agreed working hours.Weekly reporting accuracy and timely submission of technical evaluations.Knowledge Transfer Team ImprovementMinimum 2 internal knowledge sessions per month.Improvement in team AI capability scores (if assessments exist).Adoption of standard AI/ML best practices across teams.Required Qualifications7–12+ years in AI/ML, LLMs, Generative AI, NLP, Computer Vision related fields.Strong exposure to LLM tuning, RAG, embeddings, prompt optimization, and AI agents.Hands-on with Python, PyTorch/TensorFlow, vector DBs, MLflow, LangChain/LlamaIndex, etc.Experience guiding engineering teams and conducting solution audits.Strong documentation and stakeholder management skills.Experience in consulting or project-based advisory roles is preferred.DeliverablesBelow are the tangible, trackable deliverables expected from the Senior AI Consultant:1. Technical Evaluation DeliverablesAI Use Case Evaluation Reports Detailed feasibility assessment reports for each proposed AI/ML use case, including technical feasibility, risks, resources, costs, data availability, and success metrics.Architecture Review Approval Documents Final approved architecture diagrams (high-level + low-level), data flow maps, dependency diagrams, and model lifecycle design.Code Model Audit Reports Formal review summaries for code quality, ML pipeline correctness, model training approach, data preprocessing, RAG pipelines, prompt workflows, etc.2. AI Development Guidance DeliverablesWeekly Technical Review Notes Summary of reviews, feedback to dev teams, action items, timelines, and risk flags.Model Performance Evaluation Sheets Documentation of model KPIs—accuracy, latency, hallucination rate (for LLMs), retrieval quality, error analysis, and recommendations for improvement.Deployment Readiness Checklists Checklist + signoff confirming that the solution is production-ready (scalability, security, monitoring, failover, rollback, logs, alerts, etc.).3. MLOps Process DeliverablesMLOps Pipeline Review / Setup Guidance Document Recommendations for MLflow, CI/CD, versioning, monitoring, observability, model registry, testing, rollback strategy.AI Governance Best Practices Playbook A consolidated guideline for:LLMs usagePrompt templating standardsRAG architecture patternsData governanceCompliance securityModel re-training schedules4. Documentation Reporting DeliverablesWeekly Progress Reports Covering project status, blockers, risks, achievements, and next steps.Monthly Leadership Summary Report A high-level consolidated report for management, covering project health, strategy recommendations, deliverable progress, and performance metrics.Risk Register Mitigation Plan Identified risks with severity, likelihood, impact, and recommended actions.5. Training Capability Building DeliverablesTraining Materials Session Decks PPTs, demo scripts, hands-on notebooks for internal team upskilling.Minimum 2 Knowledge Sessions per Month Covering LLMs, GenAI, prompt engineering, vector databases, MLOps, or project-specific topics.6. Final Delivery Approval (Mandatory)Go-Live Technical Approval A formal sign-off stating that the solution meets all technical, architectural, and performance standards.Post-Go-Live Performance Review Document Evaluating real-world performance vs. KPIs and recommending optimizations.