AI/ML Engineer

We are seeking an experienced AI/ML Engineer with a strong foundation in machine learning and practical expertise in Generative AI. The ideal candidate will be responsible for designing, developing, and deploying production-grade ML and GenAI applications that deliver measurable business value. This role involves working beyond proof-of-concept models, handling everything from classical model development and evaluation to building Retrieval-Augmented Generation (RAG) pipelines and orchestrating multi-agent systems. Ensuring reliability and implementing safeguards in production environments are key aspects of this position.

Key Responsibilities

- Apply core machine learning techniques to solve business problems, including problem framing, data preparation, feature engineering, and model training, tuning, and validation.
- Design, build, and deploy multiple production-grade Generative AI applications using Python and agentic frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or CrewAI.
- Manage the entire GenAI lifecycle, including prompt engineering, RAG pipeline design, large language model (LLM) evaluation, and fine-tuning.
- Architect multi-agent systems incorporating supervisor and parallel patterns, human-in-the-loop processes, and agent memory management.
- Implement production safeguards such as guardrails, content moderation, and mitigation strategies for prompt injection and data leakage.
- Deploy and monitor models and LLM applications in production with a focus on drift detection, quality assurance, and cost management.
- Follow sound software engineering practices including system design, API development, testing, and continuous integration/continuous deployment (CI/CD).
- Utilize cloud AI services like Azure OpenAI, AWS Bedrock, or GCP Vertex AI, along with vector databases such as Pinecone, Weaviate, or pgvector.
- Define and track evaluation metrics to demonstrate and improve business impact and model quality over time.
- Collaborate effectively with engineers, project leads, and client stakeholders, contributing to design reviews and technical documentation.

Required Qualifications

- 5 to 7 years of experience in AI/ML engineering with a strong core machine learning background and hands-on Generative AI expertise.
- Proficiency in supervised and unsupervised learning, statistics, feature engineering, model training, evaluation, and tuning using frameworks like scikit-learn, PyTorch, or TensorFlow.
- Proven track record of building and shipping multiple production-grade ML and GenAI applications that have delivered demonstrable business impact, beyond proof-of-concept stages.
- Strong Python programming skills and practical experience with agentic frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or CrewAI.
- Hands-on experience across the GenAI lifecycle, including prompt engineering, RAG pipeline design, LLM evaluation, and fine-tuning.
- Experience with multi-agent orchestration, including supervisor, parallel, human-in-the-loop workflows, and agent memory management.
- Knowledge of production safeguards, including guardrails, content moderation, and mitigation of prompt injection and data leakage.
- Solid software engineering fundamentals, including system design, API development, and CI/CD pipelines.
- Familiarity with at least one cloud AI platform (Azure OpenAI, AWS Bedrock, or GCP Vertex AI) and one vector database (Pinecone, Weaviate, or pgvector).
- Strong communication skills to effectively engage with clients and cross-functional teams.

Preferred Qualifications

- Experience working with Databricks.
- Familiarity with LLM MLOps tooling.
- Experience developing AI solutions for business intelligence using Databricks.

This role offers the opportunity to work on cutting-edge AI technologies in a dynamic environment, contributing directly to impactful AI-driven business solutions.