About the role
At our company, we hold an uncompromising stance against mediocrity, committing ourselves to work that delivers audacious impact and is fueled by a pioneering spirit of continuous progress and innovation. This ambition is supported by leadership grounded in courage and clarity—ensuring alignment, accountability, and purpose in everything we do. Integrity shapes how we hire, recognize, and develop talent, with a focus on potential, ambition, and long-term growth. Together, we operate with collective strength, prioritizing meaningful results over unnecessary rituals and navigating challenges with unity and resolve. Above all, we remain steadfast and resilient, never losing sight of our commitments and continuously pushing forward through complexity with determination and purpose to create a better life for all.
Define the architecture, standards, and engineering direction of enterprise Generative AI platforms and applications.
Architect scalable, secure, and cost-efficient enterprise GenAI platforms, AI infrastructure, and reusable AI services.
Establish standards and best practices for LLMOps, RAG pipelines, agent orchestration, prompt management, AI governance, model evaluation, and AI observability.
Lead development of enterprise AI copilots, conversational BI systems, AI-powered customer service assistants, recommendation and personalization engines, knowledge retrieval systems, and autonomous workflow agents.
Build advanced orchestration workflows involving multiple AI agents and tools, and drive production deployment and scaling of GenAI systems.
Mentor ML and GenAI engineers across the AI Excellence Team and partner with business leaders to translate high-impact opportunities into scalable AI solutions.
Evaluate emerging GenAI technologies, models, and frameworks, and influence enterprise AI strategy, governance, and roadmap.
Lead performance optimization for latency, throughput, GPU utilization, inference cost, and reliability, while ensuring compliance with security, privacy, and responsible AI standards.
Academic Qualification
Bachelor’s or Master’s Graduation in Computer Science/Applied Statistics/Business/Relevant Field
Requirements
- Strong expertise in Python and backend engineering, with proven ability to architect distributed systems and operationalize production-scale GenAI applications.
- Strong hands-on experience with LLM application development, prompt engineering frameworks, RAG architectures, AI agents and tool-calling systems, semantic search and embeddings, AI evaluation and guardrails, hallucination mitigation, and context/memory management.
- Experience with GenAI frameworks and ecosystem tools such as LangChain, LlamaIndex, FastAPI, Docker, Kubernetes, MLflow, and CI/CD and DevOps workflows.
- Experience with AI infrastructure including GPU-based inference systems, open-source LLM deployment, fine-tuning techniques (LoRA/PEFT), vector databases, distributed AI workloads, and cloud-native architectures.
- Hands-on familiarity with major cloud platforms (Amazon Web Services, Google Cloud, Microsoft Azure).
- Experience leading technical teams or AI engineering squads, with exposure to AI governance, responsible AI, and enterprise compliance frameworks.
- Ability to think strategically while remaining hands-on technically, drive execution across multiple AI initiatives, and deliver with minimal supervision.
- Strong stakeholder influence, business communication, and presentation skills. Contributions to open-source projects, patents, conferences, or published research are highly valued.
Skills
How to apply
Apply through the employer using the Apply button. Include a current CV and, where relevant, links to your GitHub or portfolio. You will leave DataJobs BD; applications go to the employer's own site or email.