Description

In this role, you will architect and deploy AI workflows that enhance various business functions. You will collaborate with stakeholders to translate operational challenges into effective technical solutions, ensuring reliability and performance in production environments.

Requirements

• •ML intuition, engineering capability, product mindset, and strong communication skills
• •Hybrid applied ML engineer, MLOps builder, product thinker
• •Thriving in ambiguous environments
• •STEM degree or dual-degree blending business and ML/data science
• •2–5 years in applied ML, AI/data consulting, ML engineering, or MLOps
• •Proficiency in Python and SQL
• •Experience with Docker, AWS, CI/CD, and deploying ML systems
• •Experience building and deploying ML models
• •Ability to evaluate and debug ML systems using appropriate metrics
• •Experience delivering ML or automation projects end-to-end
• •Familiarity with LLMs, prompting, RAG, orchestration, fine-tuning
• •Experience building agentic or multi-step LLM systems
• •Working knowledge of vector databases
• •Experience with workflow automation platforms
• •Exposure to RL concepts
• •Experience in client-facing AI/ML consulting engagements

Tasks

• •Understand business workflows and requirements
• •Design systems to address operational challenges
• •Build agentic and LLM solutions
• •Deploy and monitor solutions in production
• •Iterate based on performance feedback
• •Design and implement production-grade agentic systems
• •Build multi-step agents using LLMs and orchestration frameworks
• •Integrate classical ML models into workflows
• •Deploy ML models in production environments
• •Create generative AI components as needed
• •Optimize prompts and retrieval strategies
• •Ensure stability and reliability of automations
• •Partner with various departments to identify automation opportunities
• •Translate business needs into technical specifications
• •Evaluate ROI and feasibility of solutions
• •Manage solutions through the full lifecycle
• •Communicate technical decisions to stakeholders
• •Build CI/CD pipelines for models and tools
• •Deploy services using Docker and AWS
• •Implement evaluation frameworks for system performance
• •Monitor systems for drift and degradation
• •Maintain well-documented codebases and diagrams
• •Track technological trends for build vs. buy decisions
• •Train end-users on new systems and gather feedback
• •Refine workflows with business functions
• •Advise leadership on automation and architecture
• •Build systems that enhance company operations
• •Translate complex workflows into AI/ML systems
• •Shape automation strategy within an AI lab

Tools & Technologies

Benefits