AI/ML Engineer

Senior AI/ML Engineer (

About the Role

We are looking for an experienced AI/ML Engineer with 2-4 years of experience, specializing in agentic AI systems, custom LLM development, and end-to-end machine learning solutions. You will architect intelligent agent workflows, fine-tune or train domain-specific large language models, and integrate advanced AI automation into production applications. This role is ideal for engineers who enjoy solving complex problems using the latest AI innovations.

Key Responsibilities

AI Agentic Systems

Design, build, and deploy agentic AI workflows using frameworks such as LangChain, AutoGen, LlamaIndex, Haystack, or similar.

Develop multi-agent systems with autonomy, planning, memory, tool-use, and reasoning capabilities.

Integrate agents with APIs, RAG systems, vector databases, and external tools to enable complex task automation.

Optimize agent behavior for reliability, efficiency, and safety.

Custom LLM Development

Fine-tune, adapt, or train domain-specific LLMs, including supervised fine-tuning (SFT), RLHF, DPO, and LoRA-based methods.

Build RAG pipelines, embeddings, and retrieval systems to enhance model performance.

Evaluate and benchmark LLMs using accuracy, hallucination reduction, robustness, and latency metrics.

Deploy LLMs in production using optimized serving stacks (e.g., vLLM, TensorRT-LLM, DeepSpeed).

Machine Learning Engineering

Build robust end-to-end ML pipelines (data ingestion ? processing ? modeling ? deployment ? monitoring).

Work with large, complex datasets for training and evaluation.

Implement MLOps best practices for automated model training, versioning, and monitoring.

Collaborate with cross-functional teams to integrate AI models into production applications.

Required Qualifications

Bachelor's or Master's in Computer Science, AI/ML, Data Science, or related fields.

Strong proficiency in Python and ML/AI frameworks:

PyTorch, TensorFlow

Hugging Face Transformers

LangChain / AutoGen / LlamaIndex / similar agent frameworks

Experience building and deploying custom LLMs or fine-tuned models.

Solid understanding of RAG, embeddings, vector databases (Pinecone, Milvus, Weaviate, Chroma).

Experience with cloud platforms (AWS/GCP/Azure) for scalable training and deployment.

Familiarity with Docker, Kubernetes, CI/CD pipelines, and MLOps tools (MLflow, Kubeflow, Airflow).

Excellent knowledge of algorithms, optimization, and evaluation metrics.

Preferred Qualifications

Experience with generative AI applications (chatbots, agents, code generation).

Knowledge of GPU optimization, quantization (4-bit/8-bit), and high-performance model inference.

Experience training models on distributed compute (DeepSpeed, FSDP, Ray).

Understanding of AI safety, alignment, and prompt engineering best practices.

Contributions to open-source AI/LLM projects.

If you are interested send your updated resume to [HIDDEN TEXT]