Machine Learning Engineer

Gain hands-on experience across the entire model lifecycle — from data preparation and training to deployment and monitoring.

Requirements

• Architect E2E Agentic Workflows: Design complex systems where AI agents collaborate to solve multi-step business problems.
• Agent Orchestration: Implement robust orchestration layers using frameworks like LangGraph, AutoGen, or AgentSpace etc... Manage state, memory, and sequential handoffs between agents.
• Hybrid Agent Strategy: Seamlessly integrate "Out-of-the-Box" Agents (e.g., Domain Specific Agents, Salesforce Agentforce) for standard tasks with highly specialized Custom Agents built on proprietary data.
• Glass-Box Observability: Engineer "Glass Box" transparency. Implement deep tracing and clear logging to visualize the "Chain of Thought," ensuring we understand why an agent made a decision, not just what it decided.
• Tool Use & Function Calling: Enable agents to interact with external APIs, databases, and internal tools to execute actions autonomously.
• Core Modeling: Design and deploy standard algorithms for Regression, Classification, Forecasting, and Clustering.
• Feature Engineering: Perform deep exploratory data analysis (EDA) and feature selection to determine which variables drive business value.
• Statistical Rigor: Display and interpret standard data science statistics (p-values, confidence intervals, error analysis) to validate model reliability before production.
• Continuous Optimization: Regularly rebuild and retrain production models to combat drift and improve accuracy/efficiency.
• LLM Implementation: Research and implement practical applications using Large Language Models (LLMs), focusing on RAG (Retrieval-Augmented Generation) and fine-tuning.
• Risk Mitigation: Actively monitor and mitigate risks associated with GenAI, including hallucination, bias, and data privacy vulnerabilities.
• Evangelism: Translate complex AI concepts into accessible language for stakeholders. Prototype new AI services quickly to showcase capabilities to customers.
• Production Deployment: Containerize models using Docker and orchestrate them via Kubernetes.
• API Development: Build robust APIs (FastAPI/Flask) to serve predictions and agent responses to integration teams.
• Standards & Procedures: Develop coding standards and CI/CD procedures for the Machine Learning platform.
• Performance Tuning: Optimize models (quantization, caching) for latency and cost efficiency.

Basic Requirements

• 4+ years of software engineering experience with a track record of delivering AI/ML models to production.
• Strong "Hybrid" Experience: You have professional experience in both Traditional ML (Scikit-learn/Pandas) and modern GenAI/LLM development.
• Agentic Framework Knowledge: Hands-on experience exploring or building with Agent orchestration tools (LangGraph, AutoGen, etc.).
• Observability Mindset: Proven ability to implement logging and tracing for complex, non-deterministic workflows.
• Containerization: Expertise with Docker and Kubernetes for deploying scalable AI solutions.
• Communication: Ability to explain complex algorithms to non-technical business partners.
• Curiosity: A self-starter who actively keeps up with the rapid changes in the AI landscape (e.g., new model releases, new agent patterns).

Referral Payment Plan