Senior ML/AI Engineer

Full Description

Alphabridge is a dynamic tech company focused on empowering startups and mid-sized businesses with innovative solutions that drive growth and scalability.

We specialize in providing cutting-edge software, strategic consulting, and technology infrastructure designed to streamline operations, enhance productivity, and foster sustainable expansion.

With a commitment to delivering tailored solutions,Alphabridge helps businesses optimize their processes and succeed in a competitive digital landscape.

About the Role

We are seeking a Senior AI/ML Engineer with 5–7 years of experience at the intersection of data engineering and applied AI.

The ideal candidate will combine a strong foundation in classical machine learning with hands-on expertise in Generative AI (LLMs, RAG, agentic workflows) and cloud-scale model deployment.

This is a highly collaborative, technical, and strategic role focused on building scalable, production-grade AI systems.

Core Responsibilities:

• Design, deploy, and maintain scalable data and AI solutions on cloud environments (AWS, Azure, or GCP).

• Lead the development and orchestration of data and ML pipelines, leveraging frameworks like Apache Airflow, Databricks, Azure Data Factory, or similar tools.

• Develop, fine-tune, and integrate Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.

• Implement AI automation workflows, chatbots, and agentic systems using tools such as LangChain, LangGraph, and vector databases (e.g., Pinecone, FAISS, Chroma).

• Collaborate with cross-functional teams to ingest, cleanse, transform, and store large and complex datasets.

• Build and deploy ML models for predictive analytics and generative tasks using TensorFlow, PyTorch, and scikit-learn.

• Ensure data quality, governance, and compliance across all engineering and AI workflows.

• Conduct code reviews, performance tuning, and technical mentorship for junior engineers.

• Maintain comprehensive documentation for data pipelines, AI workflows, and deployment architectures.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or AI/ML-related field.

• 5–7 years of professional experience in Data Engineering or Machine Learning roles.

• Strong understanding of classical ML algorithms, deep learning, and Generative AI concepts.

• Proven experience in deploying, scaling, and monitoring ML models in cloud environments (AWS, Azure, or GCP).

• Proficiency in Python and major ML frameworks: TensorFlow, PyTorch, scikit-learn.

• Hands-on experience with LangChain, LangGraph, RAG pipelines, and LLM fine-tuning.

• Familiarity with data orchestration tools (Airflow, dbt, Prefect) and data processing frameworks (Spark, Databricks).

• Strong skills in SQL, NoSQL, and data modeling for analytical workloads.

• Excellent problem-solving, communication, and collaboration skills.

Nice to Have:

• Experience with Docker, Kubernetes, and CI/CD for MLOps pipelines.

• Knowledge of embedding models, vector databases, and AI agent orchestration frameworks.

• Exposure to data visualization tools like Power BI, Looker, or Tableau.

• Understanding of workflow automation, data privacy, and security best practices for AI-driven systems.