Highlights
Design scalable AI systems, fine-tune models efficiently, build agent-based workflows.
Description
Job Summary
pWe are seeking a hands-on AI/ML engineer to design, deploy, and maintain intelligent automation systems. This role involves building scalable systems combining ML models, LLMs, and rule-based logic for real-world production use cases.
Responsibilities
- Understand business problems and design AI-driven automation solutions.
- Architect scalable systems combining ML models, LLMs, and rule-based logic.
- Collect, clean, and preprocess structured and unstructured data.
- Build pipelines for document ingestion, embeddings, and retrieval systems.
- Develop and fine-tune ML, NLP, and Generative AI models.
- Design and implement RAG (Retrieval-Augmented Generation) pipelines.
- Build agent-based systems using LangChain or similar frameworks.
- Evaluate models' precision, recall, F1-score, and LLM-specific eval methods.
- Build and deploy APIs with Flask / FastAPI.
- Monitor performance (accuracy, latency, cost) and continuously improve systems.
Required Skills
- Python programming
- ML frameworks (PyTorch / TensorFlow)
- LLMs & SLMs experience
- Fine-tuning techniques (LoRA, PEFT)
- Flask / FastAPI APIs
Required Skills Explained
- Python programming for building and deploying AI/ML models
- Experience with ML frameworks like PyTorch or TensorFlow
- Leveraging Large Language Models (LLMs) and Small Language Models (SLMs)
- Designing and implementing Retrieval-Augmented Generation (RAG) pipelines
- Building agent-based systems using LangChain, LangGraph, or similar frameworks
- Deploying APIs with Flask / FastAPI and integrating PostgreSQL and vector databases
- Evaluating models for precision, recall, and F1-score
- Maintaining and optimizing AI systems for production readiness
Who is this for
pThis role is ideal for experienced AI/ML engineers who are passionate about building intelligent automation systems and modern LLM-powered applications. Candidates should have a strong background in ML, NLP, and RAG pipelines.
Why This Job is a Good Opportunity
ulliOpportunity to work on cutting-edge technologies like LLMs and RAG pipelinesliDiverse responsibilities across multiple AI/ML domains, offering broad skill developmentliPotential for innovation and creative problem-solving in real-world applicationsliCollaboration with experienced professionals in the field of AI/MLliChance to work on projects that can significantly impact modern automation systems
Interview Preparation Tips
- Prepare examples of how you have implemented similar projects in your previous roles
- Study the latest advancements in LLMs and RAG pipelines to show your enthusiasm for cutting-edge technology
- Mention your experience with model fine-tuning techniques like LoRA or PEFT
- Discuss how you have integrated APIs using Flask / FastAPI in past projects
- Cite specific instances where automation and decision engine systems were effectively utilized
Career Growth in This Role
ppThis role offers significant opportunities for career growth. As an AI/ML engineer, you will not only be contributing to the development of intelligent automation systems but also learning and applying new technologies like LLMs and RAG pipelines. The hands-on experience with various tools and frameworks will prepare you for advanced roles in MLOps or even leading your own team.ppWith a focus on deploying AI models into production environments, the role equips you with practical knowledge that is highly valued by employers. Additionally, working on diverse projects can enhance your problem-solving skills and broaden your technical expertise, making you a strong candidate for roles in both research and product development within the tech industry.
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Skills
Frequently Asked Questions
What programming languages are preferred?Python is the primary language, with proficiency in ML frameworks like PyTorch or TensorFlow.
Do I need experience with knowledge graphs?While not required, familiarity with Neo4j or similar tools can be beneficial.
What kind of systems are you building?We focus on scalable RAG pipelines, agent-based workflows, and hybrid AI solutions for real-world applications.
