AI strategies Implementation Technical Manager

Hiring AI Strategies Implementation Technical Manager Experience: 14 to 20 Years Location: Hyderabad Notice: 0 to 30 days Strategy & solution design Translates business problems into feasible AI/ML solutions; evaluates build-vs-buy vs. fine-tune decisions; selects appropriate models, frameworks, and platforms (LLMs, traditional ML, computer vision, etc.) based on use case, cost, and latency needs; defines technical roadmaps for AI adoption across the organization. Implementation & delivery management Leads end-to-end delivery of AI projects from pilot to production; manages scope, timelines, and resourcing across data science, engineering, and product teams; runs agile/iterative delivery cycles suited to the experimental nature of AI work; de-risks projects by sequencing quick wins ahead of harder bets. Technical architecture oversight Ensures solutions are designed for scalability, maintainability, and integration with existing systems; oversees MLOps/LLMOps pipelines data ingestion, model training, evaluation, deployment, and monitoring; reviews architecture decisions around vector databases, RAG pipelines, model hosting (cloud vs. on-prem), and API integrations. Data governance & quality Ensures data pipelines feeding models are reliable, well-governed, and compliant; partners with data engineering on data quality, lineage, and access controls; addresses bias, fairness, and representativeness in training data. Model evaluation & risk management Establishes evaluation frameworks for accuracy, hallucination rates, and business KPIs; manages AI-specific risks model drift, bias, security (prompt injection, data leakage), and explainability; ensures compliance with emerging AI regulations and internal responsible-AI policies; sets up human-in-the-loop review where needed. Vendor & tooling management Evaluates and manages relationships with AI vendors and platform providers (OpenAI, Anthropic, AWS Bedrock, Azure AI, etc.); negotiates SLAs, cost structures, and data privacy terms; benchmarks tools against internal needs. Cross-functional stakeholder management Acts as the bridge between technical teams, business stakeholders, and leadership; translates technical constraints and capabilities into business language; manages expectations around what AI can and cannot realistically do; drives change management and user adoption. Team leadership Manages or coordinates data scientists, ML engineers, and AI engineers; mentors team members on best practices; fosters a culture of experimentation balanced with production discipline; conducts performance reviews and skill development planning. Monitoring & continuous improvement Sets up post-deployment monitoring for model performance, cost, and drift; runs feedback loops to retrain/improve models; tracks ROI and business impact of deployed AI systems; iterates based on user feedback and changing data patterns. Security & compliance Ensures AI systems meet data privacy regulations (GDPR, CCPA, or sector-specific rules); implements guardrails against misuse, prompt injection, and unauthorized data exposure; particularly relevant given defense/public-sector context (Cubic), ensures alignment with frameworks like NIST AI RMF or DoD AI ethics principles.