Job Description:
Overview

We are seeking an innovative and results-oriented Mid-Level AI/ML Engineer to join our dynamic team. This role is crucial for transforming novel concepts into robust, production-ready AI solutions. The ideal candidate possesses a strong background in Machine Learning engineering, extensive experience with cutting-edge LLMs and cloud-based AI services, and a commitment to maintaining high-quality, responsible AI systems.

Key Responsibilities

• Full ML Lifecycle Management: Drive projects from initial ideation to production deployment, including data pipeline development, model training, validation, and serving.
• LLM & Agentic Development: Design, implement, and optimize solutions utilizing Large Language Models (LLMs) and developing sophisticated Agentic AI systems to solve complex business problems.
• Platform Expertise: Leverage and integrate core generative AI platforms, including Gemini and Amazon Bedrock, to build scalable and efficient solutions.
• MLOps & Tools: Implement MLOps best practices, utilizing tools like MLFlow for experiment tracking, model versioning, and pipeline orchestration.
• Quality Assurance: Develop and execute comprehensive testing strategies for LLM applications, including utilizing frameworks like DeepEval for prompt engineering and model output quality.
• Analytical Skill: Apply strong analytical skills to evaluate model performance, diagnose issues, and iterate on solutions to achieve maximum business impact.
• Collaboration: Work closely with cross-functional teams (data scientists, product managers, and software engineers) to define requirements and deliver integrated AI features.

Required Qualifications

• Experience: 4-7 years of professional experience in Machine Learning Engineering, AI Development, or a closely related field.
• Education: Master’s degree in Computer Science, Data Science, Engineering, or a quantitative field.
• Technical Proficiency:
• Expertise in Python and core ML/Data Science libraries (e.g., PyTorch, TensorFlow, Scikit-learn).
• Proven experience in deploying models on major cloud platforms (GCP, AWS, or Azure).
• Deep understanding of the architecture and fine-tuning of Large Language Models.
• Domain Knowledge: Practical experience with MLOps tools (e.g., MLFlow) and validation frameworks (e.g., DeepEval).
• Problem Solving: Demonstrated ability to apply analytical skills to complex, ambiguous problems and translate insights into actionable engineering solutions.

Preferred Qualifications

• Hands-on experience developing applications or services using Google's Gemini API or models.
• Direct experience with AWS services related to AI/ML, particularly Amazon Bedrock.
• Experience in building and managing multi-step, reasoning-based Agentic AI systems.
• Prior experience in optimizing models for latency and cost efficiency in a production environment.