AI Engineer (Python)

Job Description

Job Description

We’re looking for a software engineer with a strong foundation in Python and deep experience in Artificial Intelligence and Machine Learning, particularly in the areas of Large Language Models (LLMs) and Computer Vision. The right candidate will have a proven track record of building, testing, and scaling Python-based systems, along with a solid grasp of AI / ML methodologies and their practical applications.

• Analyze software requirements and evaluate technical solutions to meet functional goals.
• Stay current with emerging research and publicly available models in LLMs, Computer Vision, and multimodal AI.
• Collaborate with peers to assess and refine proposed technical strategies.
• Develop Python-based microservices aligned with architectural designs.
• Conduct testing and integrate services into the broader software ecosystem.
• Maintain thorough documentation of research findings and implementation details.
• Contribute to a collaborative and knowledge-sharing team culture.
• Eligible for a security clearance.
• 2 to 5 years of practical experience in Python software development.
• Bachelor’s degree in Computer Science, Data Science, or a closely related discipline.
• Proficiency with machine learning libraries such as Pandas, NumPy, PyTorch, and spaCy.
• Familiarity with LLM tools and ecosystems like Langchain, LlamaIndex, and providers such as sglang, vLLM, and OpenAI.
• Experience working in environments like Jupyter Notebooks, Google Colab, and visualization tools such as Matplotlib, Plotly, and geoplotlib.
• Understanding of transformer models, embedding techniques, transfer learning, and fine-tuning.
• Solid foundation in statistical analysis and machine learning approaches including clustering, regression, neural networks, and deep learning.
• Strong communication skills, both written and verbal, with the ability to convey technical concepts to stakeholders.
• Willingness to travel occasionally for client engagements.
• Exposure to Agentic AI frameworks (e.g., langgraph) and concepts like MCP and Retrieval-Augmented Generation (RAG).
• Knowledge of natural language processing techniques such as TF-IDF, Bag of Words, Named Entity Recognition, and Part-of-Speech tagging.
• Experience with distributed computing and data platforms like Spark, Hive, MongoDB, and MapReduce.
• Understanding of modern cloud-native architectures and tools such as Docker, AWS, GCP, Jenkins, and TeamCity.
• Master’s degree in a relevant technical field is a plus.

Apache Spark, Python, Apache Hadoop, Apache Kafka, ETL - Extract Transform Load