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| Job Summary: |
| 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: |
| 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 and Experience: |
| 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): o Languages: Expert proficiency in Python familiarity with lower-level languages (C++/Rust) or Go for performance-critical components is preferred. o AI Frameworks: Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference (e.g., Hugging Face ecosystem). o 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. |
| Preferred 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): o Languages: Expert proficiency in Python familiarity with lower-level languages (C++/Rust) or Go for performance-critical components is preferred. o AI Frameworks: Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference (e.g., Hugging Face ecosystem). o 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. |
| Education & Shift timings |
| B.Tech or B.E, in Computer science, software engineering. Work Model: Willingness to align with the eClerxs guidance on WFO-WFH models. Shift Timings: Alignment with the groups work timings (1:00 PM to 10:00 PM IST). |
eClerx provides business process management, automation and analytics services to a number of Fortune 2000 enterprises, including some of the world's leading financial services, communications, retail, fashion, media & entertainment, manufacturing, travel & leisure, and technology companies.
Job ID: 144883485
