AI, Data Engineering – Tech Lead III

Jobtailor; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Jobtailor in Bengaluru invites an experienced AI Engineer to lead data pipeline design, model optimization, and LLM deployment for enterprise-grade AI solutions. You will collaborate with data scientists, product managers, and ML engineers to build scalable AI systems and deliver high-performance NLP capabilities.

Candidates should have 5+ years in AI engineering, strong Python, ML frameworks, and cloud/Kubernetes experience, with a track record in LLM deployment and MLOps.

Qualifications

• Minimum 5+ years in AI Engineering.
• Advanced Python with NumPy, Pandas, scikit-learn and DL frameworks (PyTorch, TensorFlow).
• Extensive experience with LLM frameworks (Hugging Face, LangChain) and prompt engineering.
• Experience with Spark for large-scale data analytics.
• Version control and experiment tracking using Git and MLflow.
• Software Eng: Python, Go or Rust, microservices, TDD, and concurrency.
• DevOps: IaC (Terraform, CloudFormation), CI/CD (GitHub Actions, Jenkins), Kubernetes with Helm and service mesh.
• LLM infra: vLLM, FastAPI, model quantization, vector DBs.
• MLOps: containerization for ML workloads; TorchServe or TF Serving; automated retraining.
• Cloud: AWS, GCP, Azure; secure ML systems.
• LLM projects: chatbots, recommendations, translation, optimization for performance and security.
• General: Python, SQL, ML frameworks, AWS/GCP.
• Experience creating LLD for provided architecture; microservices-based design.

Responsibilities

• Collaborate with cross-functional teams to acquire, process, and manage data for AI/ML model integration.
• Design and implement robust data pipelines to support AI/ML models.
• Debug, optimize, and enhance ML models for quality and performance.
• Operate Kubernetes with advanced configurations for scalable ML workloads.
• Build scalable LLM inference architectures with GPU memory optimization and quantization.
• Engage in prompt engineering and fine-tuning of LLMs for semantic retrieval and chatbots.
• Document model architectures, hyperparameter experiments, and validation results with Git/MLflow.
• Investigate cutting-edge LLM optimization techniques like quantization and distillation.
• Collaborate with stakeholders to develop NLP solutions: text classification, sentiment, topic modeling.
• Stay updated with AI trends and integrate new methodologies.
• Contribute to specialized AI solutions in healthcare leveraging domain knowledge.

Skills

Python & ML libraries

LLM frameworks

Spark

Git & MLflow

Go/Rust

Kubernetes & Helm

LLM serving & FastAPI

TorchServe/TF Serving

Cloud platforms (AWS/GCP/Azure)

NLP & text analytics

Microservices & concurrency

LLD & architecture design

Tools

Git

MLflow

DVC

Terraform

CloudFormation

Kubernetes

Helm

vLLM

FastAPI

TorchServe

TF Serving

LangChain

Job description

Responsibilities

• Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.
• Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.
• Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.
• Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.
• Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.
• Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development.
• Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC.
• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.
• Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.
• Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.
• Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment.

Requirements

• Minimum relevant experience - 5+ years in AI Engineering
• Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)
• Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques
• Experience with big data processing using Spark for large-scale data analytics
• Version control and experiment tracking using Git and MLflow
• Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.
• DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.
• LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.
• MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining.
• Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.
• LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.
• General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.
• Experience in creating LLD for the provided architecture.
• Experience working in microservices-based architecture.