Artificial Intelligence Specialist

Description :

Job Title : Artificial Intelligence Specialist

Location : Delhi / Noida (Hybrid)

Experience : 5+ Years of specialized experience in AI/ML and Full-Stack Engineering

Job Summary :

We are seeking a visionary, hands-on AI Architecture with a strong Full-Stack Development background to spearhead the design, development, and deployment of enterprise-grade intelligent systems. In this role, you will bridge the gap between complex data science and robust software engineeringdeveloping cognitive applications, generative AI solutions, and automated pipelines that translate raw data into actionable business intelligence.

You will not only architect the underlying AI models (NLP, Deep Learning, GenAI) but also build the automated MLOps frameworks, APIs, and scalable cloud infrastructure required to serve these models securely at an enterprise scale.

Key Responsibilities :

1. Enterprise AI Architecture & Design :

- Create detailed, end-to-end architectural blueprints for AI systems, covering data ingestion pipelines, model deployment strategies, and seamless integration with legacy IT infrastructure.

- Design and implement advanced GenAI patterns, including Large Language Model (LLM) orchestration, Retrieval-Augmented Generation (RAG), and Agentic AI workflows.

- Evaluate and select cloud-native or open-source AI tools, balancing cutting-edge innovation with enterprise-grade stability, cost control, and security compliance.

2. Full-Stack Engineering & System Integration :

- Develop full-stack, data-driven applications and robust APIs (Java, Spring Boot, Python, JavaScript) to expose AI capabilities to end-users and internal systems.

- Ensure AI services integrate seamlessly with enterprise identity management (IAM), strict security protocols (VPC-SC), and centralized data repositories.

- Orchestrate automated data and model deployment environments using Infrastructure as Code (IaC) via Terraform and CI/CD pipelines (Tekton).

3. MLOps & Production Enablement :

- Architect automated, production-ready MLOps pipelines and guardrails to streamline model training, evaluation, versioning, containerization, and monitoring across the organization.

- Conduct large-scale experimentation to optimize AI technologies for efficiency, accuracy, and inference speed.

- Set up cognitive automation and Intelligent Process Automation (IPA) frameworks to drive organizational self-service.

4. Data Engineering & Cloud Data Management :

- Architect scalable data pipelines to process structured and unstructured data using big data tools (GCP BigQuery, Dataflow, Dataproc, Data Fusion, PySpark, Airflow).

- Manage data persistence utilizing relational and non-relational storage solutions (PostgreSQL, Cloud SQL).

Skills & Qualifications :

Required Technical Skills :

- AI & Machine Learning : Deep Learning frameworks (TensorFlow, PyTorch), NLP, Image/Sound processing, and Reinforcement Learning.

- Generative AI & LLMOps : Experience with LLM orchestration frameworks (LangChain, LlamaIndex), RAG patterns, and model serving.

- Full-Stack Development : Backend mastery in Java (Spring Boot) and Python; frontend/scripting familiarity with JavaScript.

- Cloud Computing : Strong expertise in Google Cloud Platform (GCP) preferred (or AWS/Azure equivalent), including Vertex AI and cloud security models.

- Data Engineering : Big Data tools (Spark, Kafka, BigQuery, Airflow, PySpark), Cloud SQL, and PostgreSQL.

- DevOps & IaC : Terraform, Tekton, and Agile software development practices.

Experience & Education :

- Total Experience : 5+ years of specialized, hands-on experience in AI/ML application development and architecture.

- Background Requirement : Must have a proven track record as a hands-on developer transitioning into or operating as an AI Architect.

- Education : Bachelors Degree in Computer Science, IT, or a related technical field. Professional cloud or AI certifications are highly preferred.

Skills

Artificial Intelligence, Machine Learning, NLP, Deep Learning, MLOps, System Integration, Tensorflow, Generative AI, Python