Machine Learning Engineer

Core Responsibilities

• Design and implement traditional ML and LLM-based systems and applications
• Optimize model inference performance and cost efficiency
• Fine-tune foundation models for specific use cases and domains
• Implement diverse prompt engineering strategies
• Build robust backend infrastructure for AI-powered applications
• Implement and maintain MLOps pipelines for AI lifecycle management
• Design and implement comprehensive traditional ML and LLM monitoring and evaluation systems
• Develop automated testing frameworks for model quality and performance tracking

Required Technical Skills

LLM Expertise

• Model Fine-tuning: Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapter layers)
• Inference Optimization: Knowledge of quantization, pruning, caching strategies, and serving optimizations
• Prompt Engineering: Prompt design, few-shot learning, chain-of-thought prompting, and retrieval-augmented generation (RAG)
• Model Evaluation: Experience with AI evaluation frameworks and metrics for different use cases
• Monitoring & Testing: Design of automated evaluation pipelines, A/B testing for models, and continuous monitoring systems

Backend Engineering

• Languages: Proficiency in Python, with experience in FastAPI, Flask, or similar frameworks
• APIs: Design and implementation of RESTful APIs and real-time systems
• Databases: Experience with vector databases and traditional databases
• Cloud Platforms: AWS, GCP, or Azure with focus on ML services

MLOps & Infrastructure

• Deployment: Experience with model serving frameworks (vLLM, SGLang, TensorRT)
• Containerization: Docker and Kubernetes for ML workloads
• Monitoring: ML model monitoring, performance tracking, and alerting systems
• Evaluation Systems: Building automated evaluation pipelines with custom metrics and benchmarks
• CI/CD: MLOps pipelines for automated testing, and deployment
• Orchestration: Experience with workflow tools like Airflow.

Preferred Experience

• LLM Frameworks: Hands-on experience with Transformers, LangChain, LlamaIndex, or similar
• Monitoring Platforms: Knowledge of LLM-specific monitoring tools and general ML monitoring
• Distributed Training and Inference: Experience with multi-GPU and distributed training and inference setups
• Model Compression: Knowledge of techniques like distillation, quantization, and efficient architectures
• Production Scale: Experience deploying models handling high-throughput, low-latency requirements
• Research Background: Familiarity with recent LLM research and ability to implement novel techniques

Tools & Technologies We Use

• Frameworks: PyTorch, Transformers, TensorFlow
• Serving: vLLM, TensorRT-LLM, SGlang, OpenAI API,
• Infrastructure: Kubernetes, Docker, AWS/GCP
• Databases: PostgreSQL, Redis, Vector DBs

Application Question(s):

• Do you have Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapter layers)
• Do you have Experience with model serving frameworks (vLLM, SGLang, TensorRT)
• Do you have 4+ years of experience in Mlops

Work Location: Hybrid remote in Noida, Uttar Pradesh