Staff Machine Learning Engineer
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Requirements
• 12+ years of experience across AI, ML, and software engineering roles. • Strong coding skills in Python, Scala, or Java, with a track record of production-quality systems. • Hands-on experience in building, evaluating, and operating LLM-powered systems in production. • Strong understanding of context engineering, including information prioritization, relevance, and degradation under constraints. • Experience designing AI workflows or agentic systems, including task decomposition, orchestration, and failure handling. • Deep hands-on expertise in ML system design, NLP, feature engineering, and operational ML. • Experience deploying AI/ML systems on at least one major cloud platform. • Hands-on experience with Big Data or streaming systems (e.g., Spark, Kafka, Airflow). • Proven ability to lead through influence, mentor engineers, and drive cross-team initiatives. • Strong business and product mindset, with the ability to map AI/ML solutions to measurable outcomes. • Level Expectations • Delivers multi-quarter AI/ML initiatives impacting multiple teams and products. • Acts as a go-to expert for AI system design including context engineering, agent architectures, NLP, and ML system design. • Raises the overall AI maturity of the organization through architecture, mentorship, and standards.
Responsibilities
• Lead the design and development of AI/ML systems starting from problem framing, decision boundaries, and operational constraints. • Drive system and architecture decisions for AI and ML platforms to ensure scalability, performance, and operational excellence. • Design and evolve context engineering strategies for LLM-based systems under strict context window and latency constraints. • Architect agent-based systems including roles, interaction models, coordination strategies, and failure handling mechanisms. • Build and operate production AI systems with ownership of reliability, latency, cost, and correctness standards. • Define evaluation frameworks for assessing AI behavior beyond offline model metrics to ensure realistic performance expectations in operational environments. • Scale AI-assisted development workflows and reusable components across projects to improve engineering velocity and system quality consistently over time. • Review, approve ML designs with a focus on balancing tradeoffs between model accuracy, complexity of the overall system architecture, and potential business impact or ROI considerations. • Mentor senior engineers and data scientists in AI systems design principles, best practices for machine learning engineering, production readiness strategies, as well as ethical implications and bias mitigation techniques to raise standards across teams. • Collaborate with product management and other engineering leaders to translate ambiguous business problems into clear, actionable objectives that can be addressed through AI/ML solutions effectively. • Contribute actively to multi-quarter technical strategy discussions by aligning short-term project goals with long-term company investments in artificial intelligence technologies while considering the evolving market demands and competitive landscape for B2B SaaS platforms like Demandbase.
Benefits
• Our benefits include Group Medical, Personal Accident, and Term Life Insurance for comprehensive protection. Preventive healthcare covers dental, vision, and OPD needs, complemented by strong mental health support. We also provide a fitness benefit, car lease policy, and gratuity for long-term financial well-being. • Our Commitment to Diversity, Equity, and Inclusion at Demandbase