AI/ML Engineer

We are seeking an experienced AI/ML Engineer with a strong foundation in machine learning and practical expertise in Generative AI. In this role, you will be responsible for designing, developing, and deploying production-grade ML and GenAI applications that deliver measurable business value. Your work will span from classical model development and evaluation to advanced retrieval-augmented generation (RAG) pipelines and multi-agent orchestration. You will also implement safeguards to ensure these systems remain reliable and secure in production environments. This position is ideal for someone who has successfully transitioned models beyond proof of concept into real-world applications.

Key Responsibilities

- Apply core machine learning techniques to solve business problems, including problem framing, data preparation, feature engineering, model training, tuning, and validation.
- Design, build, and deploy multiple production-grade Generative AI applications in Python using agentic frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or CrewAI.
- Manage the full GenAI lifecycle, including prompt engineering, RAG pipeline design, large language model (LLM) evaluation, and fine-tuning.
- Architect multi-agent systems that incorporate supervisor and parallel patterns, human-in-the-loop processes, and agent memory capabilities.
- Implement production safeguards such as guardrails, content moderation, and mitigation strategies against prompt injection and data leakage.
- Deploy and monitor models and LLM applications in production, with a focus on detecting drift, maintaining quality, and managing cost efficiency.
- Apply strong 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, alongside vector databases such as Pinecone, Weaviate, or pgvector.
- Define and track evaluation metrics to demonstrate and improve business impact and solution 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 solid core ML background and hands-on Generative AI experience.
- Strong expertise in machine learning fundamentals, including supervised and unsupervised learning, statistics, feature engineering, model training, evaluation, and tuning, using frameworks such as scikit-learn, PyTorch, or TensorFlow.
- Proven track record of building and shipping multiple production-grade ML and GenAI applications with demonstrable business impact, beyond proof of concept.
- Proficient in Python and experienced with agentic frameworks like 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, and agent memory patterns.
- Knowledge of production safeguards such as guardrails, content moderation, and mitigation of prompt injection and data leakage.
- Strong software engineering skills in system design, API development, and CI/CD processes.
- Familiarity with at least one cloud AI platform (Azure OpenAI, AWS Bedrock, or GCP Vertex AI) and one vector database (Pinecone, Weaviate, or pgvector).
- Excellent communication skills for direct client interaction.

Preferred Qualifications

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

This role offers the opportunity to work at the forefront of AI innovation, contributing to impactful projects that leverage cutting-edge technologies in machine learning and generative AI.