AI strategies Implementation Technical Manager

Cubic Transportation; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Cubic Transportation is seeking a Hiring AI Strategies Implementation Technical Manager in Hyderabad to translate business problems into scalable AI/ML solutions. You will define roadmaps, lead end-to-end delivery, and oversee architecture, data governance, and risk management across data science, engineering, and product teams.

You will evaluate build-vs-buy decisions, manage vendors and tools, and drive responsible-AI practices while aligning with enterprise objectives and regulatory

Qualifications

• Senior AI leader with experience translating business problems into AI/ML solutions.
• Proven delivery of AI projects from pilot to production in cross-functional teams.
• Strong understanding of MLOps, model governance and risk management.

Responsibilities

• Define technical roadmaps for AI adoption across the organization.
• Lead end-to-end AI project delivery from pilot to production.
• Oversee scalable architecture, data governance, and model monitoring.
• Evaluate build-vs-buy vs. in-house development and vendor selections.
• Manage cross-functional teams including data science, engineering, and product.

Skills

AI strategy

Program management

Stakeholder management

Leadership

MLOps

Governance & risk

Vendor management

Data quality & governance

Architecture oversight

Tools

LLMs/AIs platforms

Vector databases

RAG pipelines

Cloud platforms (AWS/Azure/Google)

Job description

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.