Applied AI Engineer (Execution-Focused)

Job Description – 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.

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

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.

Job Types: Full\-time, Permanent

Pay: ₹600,000\.00 \- ₹700,000\.00 per year

Work Location: In person