Senior Machine Learning Engineer, Vice President

Candidate should possess deep hands-on expertise in designing, building, and deploying scalable machine learning systems, including advanced NLP and Generative AI (LLM) solutions. This position demands strong technical leadership, a quick learning ability, a proven track record in delivering high-value, production-grade AI solutions, and the capacity to mentor junior engineers.

The right candidate will be expected to be a key player in the project evolution & deployment shouldering the following responsibilities:

Work as a collaborative member of a team spread over multiple locations (India, UK, US) Understand internally published architectural guidelines to design solutions and represent them in architectural reviews. Define & communicate development standards that follow established architectural designs and perform code reviews to ensure quality standards of systems & team. Lead by example in developing exceptional quality code by doing design & code reviews. Design & develop platform functionality that is scalable & configurable as a global platform.

Key Responsibilities

• ML System Design & Architecture: Lead the design and architecture of robust, scalable, and high-performance machine learning systems, ensuring seamless integration with existing platforms.
• Production ML Model Deployment: Own the end-to-end lifecycle of deploying and operationalizing machine learning models in production environments, ensuring efficiency, reliability, and maintainability.
• Advanced AI/ML Engineering: Develop, optimize, and implement advanced machine learning algorithms and statistical models, focusing on engineering best practices for performance and scalability.
• Generative AI & NLP System Development: Engineer and integrate cutting-edge Generative AI (LLM) and Natural Language Processing (NLP) solutions. This includes designing efficient prompting strategies, developing LLM-based data augmentation techniques, and implementing Retrieval-Augmented Generation (RAG, including advanced RAG) to enhance model capabilities within production systems.
• Deep Learning Infrastructure: Design and build systems to effectively apply and deploy deep learning techniques (ANN, LSTM, CNN, BERT, XLNet, Transformers, neural & LLM-based embeddings) for state-of-the-art AI applications at scale.
• MLOps & Automation: Establish and implement MLOps practices, including CI/CD pipelines, automated testing, monitoring, and retraining strategies for ML models to ensure continuous improvement and stability.
• Performance Optimization: Optimize ML models and underlying infrastructure for computational efficiency, speed, and resource utilization.
• Technical Leadership & Mentorship: Drive technical excellence, promote best coding practices, perform code reviews, and provide mentorship to junior engineers.
• Cross-Functional Collaboration: Partner closely with data scientists, product managers, and other engineering teams to translate complex business requirements into technical ML solutions and ensure successful delivery.
• Risk Management & Compliance: Integrate risk assessment and compliance considerations into ML system design and deployment, ensuring adherence to applicable laws, regulations, and internal policies to safeguard the firm's reputation and assets.

Qualifications

• Experience:
  • 5+ years of hands-on experience in Machine Learning Engineering, MLOps, or AI system development.
  • Minimum of 2 years of direct experience in engineering and deploying Generative AI/LLM solutions in production.
• Technical Skills:
  • Deep proficiency in Python for production-grade ML development, with expertise in relevant libraries (scikit-learn, pandas, SpaCy, TensorFlow, PyTorch, Hugging Face Transformers).
  • Strong experience with PySpark for large-scale data processing and building robust data pipelines.
  • Proficiency in big data frameworks (Hadoop, Spark, Hive, Hue) and experience with streaming technologies.
  • Extensive experience with MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, Airflow, CI/CD for ML).
  • Proven experience in designing, implementing, and deploying NLP and deep learning models to production.
  • Hands-on experience with Generative AI development, including engineering prompting strategies, RAG implementation, and LLM fine-tuning and integration (e.g., Langchain, LlamaIndex).
  • Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
  • Good to have: Experience with graph neural networks, graph databases, or distributed systems for ML.
• System Design & Architecture