Key Responsibilities
Technical Leadership & Program Ownership
- 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
Education & Experience
- PhD or Masters 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
