- Lead the end-to-end design, architecture, and implementation of large-scale machine learning programs involving multiple interconnected projects

- Own the technical vision and roadmap for ML initiatives across the organization, ensuring alignment with business objectives

- Drive solutioning efforts for complex, ambiguous problems by breaking them down into actionable technical components

- Establish best practices, design patterns, and architectural standards for ML systems at scale

- Make critical technical decisions on model selection, infrastructure, tooling, and deployment strategies

- Champion production excellence by ensuring ML systems are reliable, scalable, maintainable, and cost-efficient

Goals & Metrics Ownership :

- Define success metrics and KPIs for ML initiatives, establishing clear linkage between technical work and business outcomes

- Drive a metrics-driven culture by implementing comprehensive monitoring, experimentation frameworks, and impact measurement systems

- Analyze and communicate the business impact of ML solutions through rigorous A/B testing and causal inference methodologies

- Set and track ambitious yet achievable goals for your programs, proactively identifying and mitigating risks

- Translate business objectives into quantifiable ML objectives and success criteria

Mentorship & Team Development :

- Mentor and guide junior and mid-level data scientists and ML engineers, accelerating their technical growth and career development

- Conduct code reviews, design reviews, and provide constructive feedback to elevate team quality standards

- Foster a culture of technical excellence, innovation, and continuous learning within the team

- Develop and deliver technical training sessions on advanced ML topics, tools, and methodologies

- Help shape hiring standards and participate actively in recruiting top ML talent

Stakeholder Management & Communication :

- Build and maintain strong relationships with cross-functional partners including product managers, engineers, executives, and business stakeholders

- Communicate complex technical concepts and results to non-technical audiences through compelling data storytelling

- Present strategic recommendations and technical proposals to senior leadership and executive teams

- Navigate organizational complexity to drive alignment and consensus across multiple stakeholders

- Proactively manage expectations and communicate risks, tradeoffs, and dependencies clearly

Innovation & Research :

- Stay at the forefront of ML/AI research and identify opportunities to apply cutting-edge techniques to business problems

- Publish findings through internal tech talks, external conferences, or academic papers (optional)

- Drive innovation through rapid prototyping, experimentation, and willingness to challenge conventional approaches

- Balance innovation with pragmatism, knowing when to leverage proven solutions versus exploring novel approaches

Qualifications :

Education & Experience :

- PhD or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field (or equivalent practical experience)

- 8+ years of hands-on experience in machine learning, data science, or related fields

- 4+ years of experience leading technical projects or programs with demonstrated business impact

- Proven track record of deploying ML models/ LLM Agents to production at scale

Technical Expertise :

- Expert-level proficiency in machine learning frameworks (TensorFlow, PyTorch)

- Deep understanding of ML fundamentals : supervised/unsupervised learning, deep learning, reinforcement learning, causal inference, optimization, and statistical modeling

- Strong software engineering skills with proficiency in Python and experience with production-grade code development

- Experience with knowledge graph integration, structured data extraction, or enterprise search systems

- Extensive experience with ML infrastructure and MLOps : model serving, monitoring, experimentation platforms, feature stores, and model registry

- Proficiency with big data technologies (Spark, Hadoop, distributed computing frameworks)

- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)

- Strong understanding of algorithms, data structures, and system design principles

LLM & Agent Specialization :

- Experience in specialized applications : conversational AI, code assistants, information extraction, content generation, or autonomous decision-making systems

- Experience building complex multi-agent systems with inter-agent communication and coordination

- Hands-on experience with instruction tuning, preference learning (RLHF/DPO), or continued pretraining of LLMs

- Experience with LLM observability and monitoring tools (LangSmith, Weights & Biases, Phoenix, or similar)

- Knowledge of emerging agent architectures and research (Tree of Thoughts, ReWOO, Reflexion, etc.)

- Experience with code generation models and AI-assisted development tools

- Familiarity with multimodal LLMs and vision-language models

Leadership & Soft Skills :

- Demonstrated ability to lead and influence without direct authority across organizational boundaries

- Exceptional communication skills with ability to distill complex technical concepts for diverse audiences

- Proven stakeholder management experience with senior leadership and cross-functional teams

- Strong analytical and problem-solving skills with attention to detail and business acumen

- Self-starter with ability to operate autonomously in ambiguous environments

- Track record of mentoring and developing technical talent

Preferred Qualifications :

- Experience in one or more specialized domains : LLMs, NLP, computer vision, recommendation systems, time series forecasting, ranking, or LLMs/generative AI

- Publications in top-tier conferences (NeurIPS, ICML, ICLR, KDD, CVPR, ACL, etc.) or journals

- Experience building and scaling ML/LLM platforms or infrastructure

- Background in experimentation design and causal inference methodologies

- Contributions to open-source ML projects or communities

- Experience working in high-growth technology companies or FAANG environments

- Track record of patent filings or granted patents in ML/AI

- Familiarity with ML model governance, fairness, and responsible AI practices specifically for generative AI