Play a pivotal role in building and scaling machine learning models from development to production.
Create efficient and reliable ML pipelines.
Lead the end-to-end design, development, and delivery of ML and GenAI solutions.
Design and implement advanced data pipelines and AI systems, including batch and streaming data processing.
Build and optimize robust data foundations for AI by developing high-quality, scalable ETL/ELT pipelines.
Define and institutionalize evaluation, validation, and governance frameworks for ML/GenAI systems.
Partner with business stakeholders to translate objectives into data-driven AI/ML solutions.
Establish and enforce best practices in MLOps, LLMOps, DataOps, and DevOps.
Architect and oversee scalable cloud-based data and AI platforms.
Drive experimentation strategy, including A/B testing and prompt optimization.
Provide mentorship to L4 and L5 engineers in data engineering and AI/ML development.
Lead cross-functional collaboration across data engineering, data science, platform engineering, and business teams to deliver integrated AI solutions.
Stay at the forefront of advancements in data engineering and Generative AI.
Requirements
Doctorate degree and 2 years of experience OR Master’s degree and 4 years of experience OR Bachelor’s degree and 6 years of experience OR Associate’s degree and 10 years of experience OR High school diploma / GED and 12 years of experience
Deep expertise in machine learning, deep learning, and Generative AI (LLMs, transformers, embeddings, fine-tuning techniques).
Proven track record of leading and delivering production-grade ML/GenAI systems end-to-end with measurable business impact with strong experience in designing scalable system architectures for ML and GenAI, including distributed systems and high-throughput pipelines.
Expertise in MLOps/LLMOps ecosystems (MLflow, Kubeflow, Airflow, CI/CD, Docker, Kubernetes).
Strong system design, architecture, and problem-solving skills with the ability to operate independently and lead large initiatives.
Demonstrated proficiency in leveraging cloud platforms (AWS, Azure, GCP) for data engineering solutions.
Strong understanding of cloud architecture principles and cost optimization strategies.
Proven ability to mentor and guide junior and mid-level engineers (L4/L5).
Good-to-Have Skills: Cloud certifications (AWS, Azure, or GCP) are a plus
Strong experience with big data ecosystems , including Apache Spark, Hadoop , and large-scale distributed data processing
Deep expertise in data engineering , including building and optimizing scalable data pipelines and platforms using Databricks, Spark, SQL, and Python
Advanced proficiency in Python and modern ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, or similar)
Experience designing robust evaluation and validation frameworks , including automated evaluations, human-in-the-loop systems, safety testing, and monitoring
Extensive experience with Retrieval-Augmented Generation (RAG) architectures, vector databases , and knowledge-grounded AI systems
Strong understanding of agentic AI frameworks , including orchestration, planning, memory management, and tool integration
Solid foundation in statistical modeling, experimentation design (A/B testing), and causal inference
Experience with NLP, semantic search, embeddings, and vector search systems
Familiarity with Responsible AI practices , including fairness, explainability, governance, and regulatory compliance
Hands-on experience with cloud-native AI/ML services across AWS, Azure, or GCP , including cost and performance optimization
Experience with the Databricks Lakehouse platform for enterprise-scale data engineering, ML, and GenAI workloads
Exposure to advanced evaluation techniques such as red-teaming, adversarial testing, and synthetic data generation
Strong experience in data modeling and performance tuning for both OLAP and OLTP systems
Hands-on experience with workflow orchestration tools such as Apache Airflow , and distributed processing frameworks like Apache Spark
Tech Stack
Airflow
Apache
AWS
Azure
Cloud
Distributed Systems
Docker
ETL
Google Cloud Platform
Hadoop
Kubernetes
Python
PyTorch
Spark
SQL
Tensorflow
Benefits
A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
group medical, dental and vision coverage
life and disability insurance
flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan