Associate AI/ML Engineer

Associate AI/ML Engineer

Company: Zaldern Technology

Location: Hyderabad, Telangana, India

Employment Type: Full-Time

Experience: 1–2 Years

About Zaldern

Zaldern is a technology and digital solutions company building AI-powered applications, intelligent automation systems, SaaS platforms, and enterprise software. Our focus is on developing practical AI solutions that help businesses automate workflows, improve decision-making, and operate more efficiently.

From agentic AI systems and AI-powered recruitment tools to intelligent customer support and business process automation, Zaldern combines software engineering with applied AI to solve real-world business challenges.

Our focus: Agentic AI. Built to Act.

Role Overview

We are looking for an Associate AI/ML Engineer who is passionate about artificial intelligence, Generative AI, and intelligent automation. You will contribute to developing AI agents, LLM-powered applications, workflow automation systems, and AI-enabled business products.

You will work closely with our engineering and product teams to build, integrate, test, and deploy AI solutions that can understand information, execute tasks, interact with software systems, and automate complex business workflows.

This role is suited to candidates who enjoy building practical AI applications, experimenting with emerging technologies, and turning AI concepts into functional products.

Key Responsibilities

• Develop and integrate AI-powered applications using Large Language Models (LLMs), Generative AI, and modern AI frameworks.
• Build agentic AI workflows that can reason through tasks, use tools, retrieve information, and execute defined business processes.
• Develop AI-driven recruitment and talent acquisition solutions, including candidate matching, resume parsing, job description analysis, and candidate screening assistance.
• Build intelligent customer support systems, AI assistants, chatbots, and knowledge-based question-answering applications.
• Implement Retrieval-Augmented Generation (RAG), semantic search, embeddings, and document processing pipelines.
• Automate business workflows involving recruitment, customer support, CRM operations, reporting, and document management.
• Integrate AI models with existing applications, databases, third-party services, and REST APIs.
• Develop backend components and APIs using Python and frameworks such as FastAPI or Flask.
• Experiment with open-source AI models, model APIs, prompt engineering, and structured AI outputs.
• Evaluate AI-generated results for accuracy, reliability, latency, and cost; implement appropriate testing and safeguards.
• Assist in deploying, monitoring, debugging, and maintaining AI-powered applications in development and production environments.
• Collaborate with software engineers to integrate AI capabilities into SaaS platforms and enterprise applications.
• Research emerging AI technologies and identify opportunities to improve existing products and internal operations.

Required Skills and Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.
• 1-2 years of relevant experience, including internships, academic projects, or hands-on AI development.
• Strong foundational programming skills in Python.
• Basic understanding of machine learning, data processing, and fundamental AI concepts.
• Familiarity with LLMs, Generative AI, prompt engineering, or AI-powered application development.
• Understanding of REST APIs, JSON, databases, and application integration.
• Familiarity with Git and version control.
• Strong analytical thinking, problem-solving, and debugging abilities.
• Willingness to learn new frameworks, experiment with AI technologies, and contribute to production-oriented projects.

Preferred Technical Skills

• Agentic AI: AI agents, tool calling, multi-step workflows, and frameworks such as LangGraph or LangChain.
• LLM Engineering: OpenAI-compatible APIs, open-source models, prompt design, structured outputs, and model evaluation.
• RAG and Search: Vector databases, embeddings, semantic search, document retrieval, and knowledge assistants.
• Machine Learning: Scikit-learn, Pandas, NumPy, and exposure to PyTorch or TensorFlow.
• Backend Development: FastAPI, Flask, REST APIs, and database integration.
• Automation: API-driven workflow automation, event-driven processes, and inte…