Applied AI Engineer (Execution-Focused)

Job Description Applied AI Engineer (Execution-Focused)

Role: Applied AI Engineer (Execution-Focused)

Location: Coimbatore (Work from Office)

Experience: 23 years of hands-on experience in ML/AI development with exposure to production systems and a strong execution mindset.

Overview

We are seeking an AI/ML Engineer with strong hands-on experience in machine learning LLMs NLP data processing and model deployment. The ideal candidate should be capable of building clean scalable AI architectures designing agentic workflows fine-tuning models preparing datasets and integrating LLMs into production applications.

You will contribute to and support end-to-end AI execution including model development integration evaluation and deployment with opportunities to take increasing ownership over time.

This role focuses on hands-on execution and integration not on defining AI strategy or owning system architecture independently on day one.

Key Responsibilities

1. Model Development & Fine-Tuning

Build train and fine-tune ML/NLP/LLM models for production use.

Evaluate performance using standard AI metrics and deliver measurable improvements.

Optimize models for accuracy latency and scalability.

2. AI Architecture & System Design

Contribute to clean modular AI pipelines covering data processing training evaluation and inference.

Support maintainable and extensible AI workflows by following established system patterns and guidance.

3. Prompt Engineering & LLM Integration

Create and optimize prompts structured reasoning and multi-step chains.

Integrate LLMs (OpenAI Azure Hugging Face etc.) into applications.

Evaluate LLM outputs for quality stability and reduced hallucinations.

4. Agentic Workflow Development

Build and orchestrate agent-based AI workflows.

Connect LLM agents with tools APIs memory and multi-step reasoning flows.

Support improvements in agentic workflows including tool usage reasoning steps and output quality with guidance and iteration.

5. Dataset Preparation & Validation

Prepare clean label and validate datasets for ML/LLM training and evaluation.

Identify data bias duplications and quality issues.

Build evaluation datasets aligned with product and QA expectations.

6. Deployment & Integration

Develop inference pipelines and microservices for real-time and batch predictions.

Assist with model versioning monitoring and logging to support model lifecycle maintenance in production environments.

Collaborate with engineering teams for seamless integration.

7. Research & Innovation

Stay updated with advancements in LLMs embeddings vector search and generative AI.

Evaluate and apply new architectures frameworks and techniques to enhance product capabilities.

8. Documentation & Collaboration

Document model architecture datasets experiments workflows and deployment processes.

Work with Product QA and Engineering to define AI evaluation criteria and expectations.

Participate in sprint ceremonies and cross-functional planning sessions.

Core Skills & Qualifications

Bachelors degree in computer science Data Science AI/ML or related field.

23 years of experience in ML/AI model development and deployment.

Strong Python skills (PyTorch/TensorFlow Hugging Face Scikit-learn NumPy Pandas).

Experience fine-tuning and integrating NLP/LLM models.

Strong understanding of embeddings vector search and evaluation metrics.

Experience building REST APIs and inference pipelines.

Knowledge of cloud platforms (Azure/AWS/GCP).

Ability to design clean scalable ML/LLM system architectures.

Excellent analytical thinking and communication skills.

Nice to Have

Experience with vector databases (FAISS Pinecone Chroma DB).

Experience with agentic frameworks and orchestration tools (Lang Chain Llama Index Semantic Kernel etc.).

Understanding of MLOps and CI/CD for ML systems.

Exposure to healthcare or AI-driven SaaS products.

Note: This role is not intended for Lead or Staff-level AI engineers. We value strong fundamentals hands-on execution and learning ability over deep specialization or architectural ownership at this stage.

Required Skills:

Experience with vector databases (FAISS Pinecone Chroma DB). Experience with agentic frameworks and orchestration tools (Lang Chain Llama Index Semantic Kernel etc.). Understanding of MLOps and CI/CD for ML systems. Exposure to healthcare or AI-driven SaaS products.