Artificial Intelligence Engineer

About the role:
This is a mid-to-senior level role that offers an excellent opportunity for a skilled AI Engineer to bridge the gap between experimental machine learning models and production-ready intelligent systems. As part of our outsourcing organization, you will benefit from exposure to a wide variety of industries and business functions, allowing you to develop a well-rounded skillset and drive scalable AI transformation.
Job Description:
The AI Engineer will play a key role in driving data-driven decision-making and intelligent automation within our organization. You will be responsible for the architectural design, optimization, and deployment of Agentic AI solutions and Large Language Models (LLMs/SLMs). Focusing on building robust pipelines, optimizing model performance for specific hardware constraints, and developing sophisticated agent orchestration frameworks, you will 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), implementing 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.
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.
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 & Consulting: Translate complex technical constraints (e.g., memory limits, inference speed) into clear trade-offs for client stakeholders and project leadership.
Required 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.
Education: B.Tech or B.E. in Computer Science, Software Engineering, or a related technical field.
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, with the ability to manage and version multiple "Specialist Adapters."
Preferred Qualifications:
Experience working in a cross-functional and client-facing team environment within an outsourcing or consulting framework.
Familiarity with workflow automation and enterprise analytics tools (e.g., Alteryx, advanced BI pipelines).
Work Model & Shift Timings:
Work Model: Willingness to align with eClerx’s guidance on WFO-WFH models.
Shift Timings: Alignment with the group’s work timings (1:00 PM to 10:00 PM IST).

Work arrangement
Hybrid