Fourkites - Senior Engineering Manager - Artificial Intelligence / Machine Learning
FourKites India Pvt LtdChennai13 days ago
Job descriptionLead, mentor, and grow a team of 15+ AI engineers, data scientists, and software engineers Build high-performing teams through hiring, performance management, and career development Foster a culture of innovation, collaboration, and continuous learning Conduct regular 1 : 1s, performance reviews, and career development conversations Champion diversity, equity, and inclusion initiatives within the engineering organization Partner with Product Management to define AI product roadmap and priorities Translate business objectives into technical initiatives and measurable outcomes Manage multiple concurrent AI / ML projects from conception to production deployment Establish and track KPIs for team performance, model quality, and system reliability Balance innovation with pragmatic delivery to meet business deadlines Work closely with Data Science, Product, Design, and other engineering teams Communicate technical concepts and trade-offs to non-technical stakeholders Represent engineering in executive discussions and strategic planning sessions Build relationships with external partners, vendors, and research institutions Drive alignment across teams on AI ethics, responsible AI practices, and governance Establish best practices for ML model development, testing, and deployment Implement MLOps practices for continuous integration and deployment of ML models Ensure compliance with data privacy regulations and AI governance policies Drive improvements in model monitoring, A / B testing, and experimentation frameworks Manage engineering budget and resource allocation 13+ years of software engineering experience, with 5+ years focused on ML / AI systems 5+ years of engineering management experience, including managing managers Proven track record of shipping ML products at scale in production environments Experience with full ML lifecycle : data collection, feature engineering, model training, deployment, and monitoring Deep understanding of machine learning algorithms, deep learning, and statistical methods Proficiency in ML frameworks (TensorFlow, PyTorch, JAX) and programming languages (Python, Scala, Java) Experience with distributed computing frameworks (Spark, Ray) and cloud platforms (AWS, GCP, Azure) Knowledge of MLOps tools and practices (Kubeflow, MLflow, Airflow, Docker, Kubernetes) Understanding of data engineering, ETL pipelines, and big data technologies Demonstrated ability to build and scale engineering teams Strong communication skills with ability to influence at all levels of the organization Experience driving technical strategy and making architectural decisions Track record of successful cross-functional collaboration and stakeholder management Ability to balance technical depth with business acumen Advanced degree (MS / PhD) in Computer Science, Machine Learning, or related field Deep experience with Large Language Models (LLMs), Small Language Models (SLMs), and generative AI applications Expertise in building production AI agent systems : Multi-agent architectures and swarm intelligence Memory systems : short-term, long-term, episodic, and semantic memory Planning algorithms : hierarchical planning, goal decomposition, and backtracking Tool use and function calling optimization Agent communication protocols and coordination strategies Experience with advanced agent frameworks : DSPy, Guidance, LMQL, Outlines for constrained generation Knowledge of prompt engineering techniques : few-shot, chain-of-thought, self-consistency, constitutional AI Experience with RAG architectures : vector stores, hybrid search, re-ranking, and query optimization Expertise in training techniques : supervised fine-tuning, RLHF, DPO, PPO, constitutional AI, and synthetic data generation Experience with parameter-efficient fine-tuning methods : LoRA, QLoRA, prefix tuning, and adapter layers Knowledge of model optimization techniques : quantization (INT8, INT4), distillation, pruning, and flash attention Extensive experience in dataset curation for LLM training : Web-scale data processing (Common Crawl, C4, RefinedWeb methodologies) Creating instruction-tuning datasets (Alpaca, Dolly, FLAN-style formats) Building preference datasets for RLHF / DPO training Domain adaptation and specialized corpus creation Multi-lingual and code dataset preparation Knowledge of data mixing strategies, replay buffers, and curriculum learning for optimal training Experience with data augmentation techniques : paraphrasing, back-translation, and synthetic data generation using LLMs Expertise in data decontamination and benchmark pollution prevention Experience with workflow automation platforms : n8n, Zapier, Make for business process automation Knowledge of enterprise integration patterns : event-driven architectures, saga patterns, and CQRS Strong background in data science : statistical analysis, A / B testing, experimentation design, and causal inference Experience with data mesh architectures and building self-serve data platforms Expertise in data quality frameworks, data contracts, and SLA management for data pipelines Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, FAISS) and embedding systems Knowledge of privacy-preserving ML techniques : differential privacy, federated learning, secure multi-party computation Background in specific AI domains : NLP, Computer Vision, Recommendation Systems, or Reinforcement Learning Experience with LLM evaluation frameworks and benchmarking (HELM, EleutherAI eval harness, BigBench) Hands-on experience with popular LLM frameworks : Hugging Face Transformers, vLLM, TGI, Ollama, LiteLLM Experience with dataset processing tools : Datasets library, Apache Beam, Spark NLP Publications or contributions to open-source ML projects Experience in high-growth technology companies or AI-first organizations Knowledge of AI safety, ethics, and responsible AI practices Experience with multi-modal models and vision-language models Opportunity to work on cutting-edge AI technology with real-world impact Competitive compensation package including equity Access to state-of-the-art computing resources and research tools Budget for conferences, training, and professional development Collaborative environment with talented engineers and researchers Flexible work arrangements and comprehensive benefits
Description : About the job
At FourKites we have the opportunity to tackle complex challenges with real-world impacts.
Whether its medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.
Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity.
We are seeking an experienced Senior Engineering Manager to lead our AI / ML engineering teams in building cutting-edge artificial intelligence solutions.
This role requires a unique blend of technical expertise in AI / ML, proven engineering leadership, and strategic thinking to drive innovation at scale.
Key Responsibilities :
Technical Leadership :
- Define and execute the technical strategy for AI / ML initiatives across multiple product areas
- Oversee the design and architecture of scalable ML systems, from data pipelines to model deployment
- Drive decisions on technology stack, frameworks, and infrastructure for AI / ML workloads
- Ensure engineering excellence through code reviews, design reviews, and technical mentorship
- Stay current with AI / ML research and industry trends to inform strategic decisions
People Management :
Strategic Planning & Execution :
Cross-functional Collaboration :
Operational Excellence :
Required Qualifications :
Experience :
Technical Skills :
Leadership Competencies :
Preferred Qualifications :
What We Offer :
(ref : hirist.tech)
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Senior Engineering Manager • Chennai
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