Applied AI Engineer (Execution-Focused)

Role: Applied AI Engineer (Execution-Focused) Location: Coimbatore (Work from Office) Experience: 2–3 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- Research & Innovation

- Stay updated with advancements in LLMs, embeddings, vector search, and generative AI.
- Evaluate and apply current architectures, frameworks, and techniques to enhance product capabilities.

- 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

- Bachelor's degree in computer science, Data Science, AI/ML, or related field.
- 2–3 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.

If you are interested in this opportunity, please click on the Apply Now button to submit your application.

📌 Applied AI Engineer (Tamil Nadu)
🏢 Aeonn Ark
📍 Tamil Nadu