Senior Staff Machine Learning Engineer

Role overview

Responsibilities

• check_circle Uses Generative AI models (GPT4, Claude, Gemini), other LLMs, Agents and LangChains, CrewAI, Strands to build different AI, Agents driven intelligent solutions
• check_circle Experience in building GenAI based solution in auto-pilot or co-pilot mode which run in production at scale
• check_circle Experience in building Responsible AI guardrails and metrics around traditional AI or Gen AI models
• check_circle Familiar with latest Gen AI, Agent based products and AI coding tools in the market
• check_circle Fundamental understanding of NLP, transformer, embedding space and evaluation metrics
• check_circle Design and implement finetuning SLMs, Core Machine learning models and data ingestion pipelines
• check_circle Experience in dealing with "messy" enterprise data and building ETL transformations to facilitate more effective AI decision-making
• check_circle Experience in building software products used in production
• check_circle Create and maintain optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements
• check_circle Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources
• check_circle Work with architecture, data, and design teams to assist with data related technical issues and support data infrastructure needs
• check_circle Deploy ML models in AWS environment specifically in AWS Sage Maker environment
• check_circle Perform root cause analysis for production issues where the root cause is in infrastructure, environment, configuration, or deployment routines; understand when to escalate to product development teams; remediate root causes and implement preventative actions
• check_circle Establish standards and practices around MLOps, including governance, compliance, and data security
• check_circle Experience with AI or Agent driven complex ETL generation and data transformation preferred
• check_circle Qualifications include: You’re a highly technical research engineer with a strong understanding of the latest advancements in AI, especially GenAI - LLMs and Agents You have 5+ years of professional experience in software engineering, GenAI, machine learning, or applied research, with a proven ability to drive high-impact AI initiatives end to end. Strong working knowledge of deep learning, machine learning and statistics Some experience in complex SQLs and ETL transformations Experience related to AWS services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2 Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer), machine learning, CV, GNN, or distributed training Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts Demonstrated ability to design, implement, and scale machine learning workflows (ML OPs); including deployment and delivery of production-ready model APIs Proficiency with at least one machine learning lifecycle platform (Sagemaker, MLFlow, TensorFlow, etc.), orchestration platform (Airflow, Dagster, etc.) and data platform like SnowFlake/DataBricks You have a track record of implementing cutting-edge research into robust, scalable, and well-tested code. You bring a strong engineering mindset and write clean, efficient code that performs reliably in production. 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.) Excellent verbal and written communication skills. Experience with Infrastructure-as-Code tools and frameworks Master's degree in computer science, data science, mathematics, or a related field.
• check_circle You’re a highly technical research engineer with a strong understanding of the latest advancements in AI, especially GenAI - LLMs and Agents
• check_circle You have 5+ years of professional experience in software engineering, GenAI, machine learning, or applied research, with a proven ability to drive high-impact AI initiatives end to end.
• check_circle Strong working knowledge of deep learning, machine learning and statistics
• check_circle Some experience in complex SQLs and ETL transformations
• check_circle Experience related to AWS services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2
• check_circle Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer), machine learning, CV, GNN, or distributed training
• check_circle Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts
• check_circle Demonstrated ability to design, implement, and scale machine learning workflows (ML OPs); including deployment and delivery of production-ready model APIs
• check_circle Proficiency with at least one machine learning lifecycle platform (Sagemaker, MLFlow, TensorFlow, etc.), orchestration platform (Airflow, Dagster, etc.) and data platform like SnowFlake/DataBricks
• check_circle You have a track record of implementing cutting-edge research into robust, scalable, and well-tested code.
• check_circle You bring a strong engineering mindset and write clean, efficient code that performs reliably in production.
• check_circle 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.)
• check_circle Excellent verbal and written communication skills.
• check_circle Experience with Infrastructure-as-Code tools and frameworks
• check_circle Master's degree in computer science, data science, mathematics, or a related field.

Benefits

• check_circle Comprehensive Insurance Coverage (including Medical, Dental, Vision, Pharmacy benefits, Life Insurance and AD&D)
• check_circle Flexible Spending Accounts and Health Savings Accounts
• check_circle Short-Term Disability and Long-Term Disability
• check_circle Comprehensive 401(k) plan
• check_circle Generous Parental Leave
• check_circle Unrestricted paid time off (known as Discretionary Time Off - DTO)
• check_circle Wellness Program, including ClassPass & Employee Assistance Program
• check_circle Tuition Reimbursement
• check_circle Optional Benefits: Pet Insurance, Identity Theft Protection, Student Debt Repayment Program and Prepaid Legal coverage