Senior Analyst
- eClerx
- 5 - 10 years
- Mumbai
- 3 days ago
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Job Description
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 eClerx s guidance on WFO-WFH models.
Shift Timings: Alignment with the group s work timings (1:00 PM to 10:00 PM IST).
Job Classification
Industry: Building MaterialFunctional Area / Department: Engineering - Software & QARole Category: Quality Assurance and TestingRole: Test AnalystEmployement Type: Full timeContact Details:
Company: eClerxLocation(s): Mumbai
Keyskills: System architecture C++ Automation Manager Quality Assurance Version control Prototype Machine learning SQL Python
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eClerx
eClerx Services Ltd, one of the first Knowledge Process firms listed in India (Bombay Stock Exchange: ECLERX), provides diverse and complex data analytics and customized process solutions to global enterprise Clients from our multiple India-based delivery centers. eClerx drives our Clients’ ...
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