Artificial Intelligence Engineer - Process Manager

Associate Process Manager

Description

Job Description: AI Engineer (PM / SPM)

Role Overview

We are seeking a highly skilled AI Engineer to bridge the gap between experimental machine learning models and production-ready intelligent systems. In this role, you will be responsible for the architectural design, optimization, and deployment of Agentic AI solutions and Large Language Models (LLMs/SLMs). You will focus on building robust pipelines, optimizing model performance for specific hardware constraints, and developing sophisticated agent orchestration frameworks to solve complex business automation challenges.

Key Responsibilities

1. AI System Architecture & Implementation

• Model Deployment & Optimization: Lead the end-to-end integration of machine learning models and fine-tuned SLMs into production environments, focusing on model compression, latency reduction, and hardware-specific optimization.
• Agentic Workflows: Design and implement autonomous agent architectures, including multi-step reasoning engines, tool-use integration, and structured decision-making frameworks.

• Efficient Fine-Tuning Implementation: Develop and maintain the infrastructure for Parameter-Efficient Fine-Tuning (PEFT). Implement techniques like LoRA, QLoRA, or Adapter-tuning to minimize computational overhead.

• Retrieval Augmented Generation (RAG): Build and maintain high-performance vector databases and semantic search indices to enable context-aware AI responses and sub-second data retrieval.

2. Data Engineering & Pipeline Development

• Automated Data Pipelines: Develop scalable, automated pipelines for the cleaning, normalization, and feature engineering of high-velocity raw data streams.
• Quality Assurance: Collaborate with Data Scientists to establish "Ground Truth" datasets and implement automated validation layers to ensure model output reliability and safety.
• System Monitoring: Design and implement monitoring solutions to track model drift, inference performance, and resource utilization in production.

3. Technical Leadership & Integration

• Cross-Functional Collaboration: Work closely with Data Science, Data Engineering, and DevOps teams to ensure seamless transition from model prototype to hardened production binary.
• Mentorship: Provide technical guidance and code reviews for junior engineers, championing best practices in software engineering and AI deployment.
• Stakeholder Engagement: Translate complex technical constraints (e.g., memory limits, inference speed) into clear trade-offs for client stakeholders and project leadership.

Required Skills & Qualifications

• Experience: Minimum of 5+ years of experience in Software Engineering or Machine Learning Engineering, with a proven track record of deploying AI models in production.
• Technical Stack (Expert Level):
  • Languages: Expert proficiency in Python; familiarity with lower-level languages (C++/Rust) or Go for performance-critical components is preferred.
  • AI Frameworks: Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference (e.g., Hugging Face ecosystem).
  • Data Infrastructure: Hands-on experience with SQL/NoSQL databases, Vector Databases, and cloud-native AI services (AWS, GCP, or Azure).
• Engineering Rigor: Demonstrated mastery of version control (Git), CI/CD pipelines, containerization (Docker/Kubernetes), and API design (REST/gRPC).
• Problem Solving: Proven ability to optimize models for restricted resource environments (memory, CPU/GPU limits) without compromising core performance

• PEFT & Adaptability: Deep experience with PEFT libraries (e.g., Hugging Face PEFT) and fine-tuning frameworks. Ability to manage and version multiple "Specialist Adapters."