ML Engineer

About Company:

USEReady helps businesses to be self-reliant on data. Growing over 3000% since inception, USEReady achieved #113 rank in Inc 500 2015 and honoured top 100 companies in North America 2015 by Red Herring. USEReady is built on strong entrepreneurial spirit with unprecedented opportunities for career growth. At USEReady, we believe in achieving career growth while improving our individual competencies. If you desire to be part of a team that believes in mutual success and inspiration, you are welcome to apply.

USEReady helps enterprises apply AI and agentic intelligence to improve decisions, automate operations, and build smarter, more autonomous business systems.

For more than a decade, we have built the foundations that make this possible by modernizing BI environments, migrating legacy platforms, improving data quality, and enabling governed, cloud-first architectures. These foundations now support the next step: AI-driven insights, automated intelligence, and agent-powered decision support that reduce complexity and accelerate outcomes.

We work closely with technology leaders such as AWS, Elementum, Snowflake, Tableau, Databricks, and others to help organizations modernize analytics, strengthen governance, and deploy agentic automation with confidence. Headquartered in New York City with 450+ experts across the United States, Canada, India, and Singapore, we serve industries including financial services, healthcare, manufacturing, government, education, and retail. Our deep expertise, player-coach delivery model, and focus on fast, measurable results make us a trusted partner for building an AI-ready enterprise.

Check us out at  www.useready.com

Job Description: ML Engineer

We are seeking a talented AI/ML Engineer with 2-4 years of hands-on experience to join our growing AI team. The ideal candidate will have strong expertise in modern AI technologies, including Large Language Models (LLMs), AI Agents, and agentic frameworks. You will be responsible for designing, developing, and deploying intelligent systems that solve real-world business problems while ensuring robust monitoring and governance.

Key Responsibilities:
•Design and implement machine learning solutions using fundamental ML algorithms and advanced NLP techniques
•Develop and optimize embedding models for semantic search, recommendation systems, and knowledge retrieval
•Build production-grade AI Agents using frameworks such as LangChain, LangGraph,  OpenAI Agents SDK, etc.
•Architect and implement Retrieval-Augmented Generation (RAG) systems for enterprise applications
•Fine-tune Large Language Models for domain-specific use cases and performance optimization
•Integrate AI agents with enterprise systems, APIs, and data pipelines
•Implement comprehensive monitoring and observability using LangSmith, Langfuse, and similar governance frameworks
•Establish AI governance practices including guardrails, compliance checks, and ethical AI guidelines
•Collaborate with cross-functional teams to identify AI opportunities and translate business requirements into technical solutions
•Optimize model performance, latency, and cost-effectiveness in cloud environments
•Conduct code reviews, write technical documentation, and mentor junior team members
•Strong experience with SQL databases and query optimization
•Hands-on experience with Snowflake for data warehousing and analytics

Mandatory Skills:

• Strong understanding of core ML algorithms including classification, regression, clustering, and ensemble methods

• Hands-on experience with model evaluation metrics, cross-validation, and hyperparameter tuning Proficiency in feature engineering and data preprocessing techniques

•Proven experience working with LLMs

•Expertise in prompt engineering, few-shot learning, and chain-of-thought prompting

•Experience with LLM APIs and model integration patterns

•Practical experience building autonomous AI agents

Desirable Skills:

•Experience fine-tuning open-source LLMs using LoRA, QLoRA, or full fine-tuning

•Experience with Snowflake's AI/ML features including Snowpark, External Functions, and Streamlit integration.

•Experience with MLOps practices and CI/CD pipelines for machine learning

•Familiarity with containerization (Docker) and orchestration (Kubernetes)