Artificial Intelligence Engineer

Wappnet Systems Pvt. Ltd.; Ahmedabad District; On-site; INR 1,800,000 - 3,400,000; Full time

Job summary

Wappnet Systems Pvt. Ltd. seeks an experienced AI & Machine Learning Engineer to design, build, and deploy intelligent systems.

You will own the end-to-end ML lifecycle from data ingestion and model development to production deployment and monitoring. You will design and deploy production-grade ML, CV, and NLP models, fine-tune LLMs for domain use cases, and apply state-of-the-art techniques. You will collaborate with product and data teams, implementing CI/CD and monitoring for reliable AI

Qualifications

• Proficiency in Python and ML frameworks like TensorFlow or PyTorch.
• Experience with large datasets and data preprocessing techniques.
• Familiarity with language models and generative AI is desirable.
• Knowledge of deep learning architectures including CNNs, RNNs, and Transformer-based models.
• Experience with NLP techniques, text classification, NER, and sentiment analysis.
• Proficiency in SQL and NoSQL databases.
• Hands-on experience with MLOps tools for model lifecycle management.
• Familiarity with cloud platforms for deployment and scaling.
• Experience with Docker and Kubernetes for deployment.

Responsibilities

• Design, develop, and evaluate production-grade ML, CV and LLM models.
• Fine-tune and optimize pre-trained large language models for domain use cases.
• Research and apply transformers, diffusion models, and multi-modal AI.
• Build LLM-powered applications using agent frameworks and tools.
• Implement safety, guardrails, and content moderation in AI systems.
• Create evaluation pipelines for continuous model QA.
• Build scalable data pipelines for model training and inference.
• Manage feature stores, data versioning, and preprocessing workflows.
• Collaborate with data engineers to ensure data quality and governance.

Skills

Python

TensorFlow

PyTorch

NLP

Computer Vision

Transformers

Data preprocessing

SQL

NoSQL

REST API

Docker

Kubernetes

MLflow

Weights & Biases

Kubeflow

Cloud platforms

Data versioning

Feature stores

Tools

Docker

Kubernetes

MLflow

Weights & Biases

Kubeflow

Job description

Artificial Intelligence Engineer & Machine Learning Engineer

Position Overview:

We are seeking an experienced AI & Machine Learning Engineer to design, build, and deploy intelligent systems that drive measurable business impact. You will own the end-to-end ML lifecycle from data ingestion and model development to production deployment and ongoing monitoring. Working alongside product, data, and engineering teams, you will translate complex business problems into scalable, reliable AI-powered solutions.

Key Responsibilities:

• Design, develop, and evaluate production-grade ML, Computer vision and Small, Large language models — classification, regression, NLP, Computer Vision, and Generative AI.
• Fine-tune and optimize pre-trained large language models for domain-specific use cases.
• Research, prototype, and apply state-of-the-art techniques including transformers, diffusion models, and multi-modal AI.
• Build LLM-powered applications using Agent skills, Guardrails, Agent harness engineering, MCP, Function tooling. Build agents using one or more of the following : OpenAI Agents SDK, Claude Agents SDK, Langchain Deep Agents, Haystack connectors.
• Implement safety and guardrail layers: prompt injection detection, PII redaction, content moderation, and policy enforcement.
• Create evaluation harnesses and automated benchmarking pipelines for continuous LLM quality assurance.
• Build and maintain scalable, fault-tolerant data pipelines for model training, validation, and real-time inference.
• Design and manage feature stores, data versioning, and preprocessing workflows for large-scale datasets.
• Collaborate with data engineers to ensure data quality, consistency, and governance standards.

Deployment & MLOps

• Deploy and serve ML/AI models via REST APIs and microservices using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
• Implement CI/CD pipelines for model training, testing, and deployment using tools like MLflow, Weights & Biases, or Kubeflow.
• Monitor production model performance — tracking accuracy drift, data drift, latency, and system health with alerting pipelines.
• Optimize models for production: quantization, distillation, ONNX export, and hardware-specific inference tuning.
• Partner with product managers, business stakeholders, and domain experts to define AI solution requirements and success metrics.
• Mentor junior engineers and data scientists; contribute to code reviews, architecture discussions, and technical documentation.
• Communicate complex AI concepts and findings clearly to both technical and non-technical audiences.
• Stay current with latest AI/ML research; evaluate and advocate for relevant new tools, frameworks, and methodologies.

Requirements:

Technical Skills:

• Proficiency in Python.
• Experience with machine learning frameworks like TensorFlow or PyTorch.
• Ability to work with large datasets and expertise in data preprocessing and collection techniques.
• Familiarity with language models and generative AI is highly desirable.
• Knowledge of deep learning architectures including CNNs, RNNs, and Transformer-based models.
• Experience with NLP techniques, text classification, named entity recognition, and sentiment analysis.
• Proficiency in SQL and NoSQL databases for data storage and retrieval.
• Hands‑on experience with MLOps tools such as MLflow, Weights & Biases, or Kubeflow for model lifecycle management.
• Familiarity with cloud platforms (AWS, GCP, or Azure) for model deployment and scaling.
• Experience with containerization and orchestration tools — Docker and Kubernetes.
• Knowledge of REST API development and integrating ML models into production applications.
• Understanding of data versioning, feature stores, and pipeline automation best practices.

Experience:

• Minimum 3 years of hands‑on experience in ML/AI engineering roles, building and deploying production‑grade models.
• Demonstrated ownership of the full ML lifecycle — data preparation, model development, deployment, monitoring, and iteration.
• Proven experience with NLP or Computer Vision solutions using modern architectures (BERT, GPT, YOLO, ResNet, etc.).
• Practical experience integrating and fine‑tuning large language models for real‑world applications.
• Experience deploying ML systems on at least one major cloud platform (AWS SageMaker, GCP Vertex AI, or Azure ML).

Soft Skills:

• Strong analytical and problem‑solving mindset with meticulous attention to detail.
• Excellent communication skills — able to translate technical concepts for diverse audiences.
• Collaborative team player with experience in Agile/Scrum environments.
• Self‑motivated and intellectually curious; proactive in proposing new ideas and improvements.

What We Offer:

• Competitive salary package.
• Opportunity to work with international clients and projects.
• Exposure to AI‑driven development processes.
• Professional growth and learning environment.