The Role
Design, develop, and deploy LLM-powered and Generative AI solutions including RAG pipelines, embeddings, prompt engineering, LangChain workflows, and agent systems; build AI-backed APIs and integrate models; handle data processing, vector databases, containerized deployments, basic MLOps, and implement RBAC/authentication and secure coding practices.
Summary Generated by Built In
We are looking for a Mid-Level AI/ML Engineer to design, develop, and deploy machine learning and Generative AI solutions. The role focuses on building LLM-powered applications, RAG pipelines, and AI services while working closely with product, backend, and data teams.
Key Responsibilities:
- AI / ML & Generative AIDevelop and implement machine learning and NLP models for real-world applications.Build Generative AI applications using LLMs such as OpenAI, Llama, and Mistral.Design and optimize Retrieval-Augmented Generation (RAG) pipelines.Work with embedding models, prompt engineering, and model fine-tuning.Apply ML techniques like classification, regression, clustering, and dimensionality reduction.Develop LLM workflows using LangChain and LangGraph.Work on agent-based and multi-agent systems, including basic A2A communication.Experiment with CrewAI and agent orchestration frameworks.
- Backend & API DevelopmentBuild and maintain AI-backed APIs using FastAPI, Flask, or Django.Integrate ML models into applications with scalable REST services.
- Data & DatabasesWork with data using Python, SQL, PostgreSQL, and ArangoDB.Perform data processing and analysis using Pandas and NumPy.Use vector databases such as FAISS, Milvus, or Chroma.
- Deployment & MLOpsContainerize and deploy AI services using Docker.Use Git and Linux for version control and development workflows.Support on‑premise deployments and basic model monitoring.
- Security & Access ControlImplement RBAC, authentication, and authorization using JWT and SSO.Follow secure coding and data‑protection best practices.
Required Skills:
- Programming: Python, SQL
- ML & AI: Scikit-learn, PyTorch or TensorFlow
- Generative AI: LLMs, RAG pipelines, prompt engineering
- Frameworks: LangChain, LangGraph, HuggingFace
- Backend: FastAPI / Flask / Django
- Databases: PostgreSQL, ArangoDB, Vector DBs
- Tools: Docker, Git, Linux
- Data & Visualization: Pandas, NumPy, Matplotlib, Power BI
Education:
- Bachelor’s degree in Computer Science, AI, Data Science, or related field
- 5–8 years of experience in AI/ML or Data Science roles
Skills Required
- Bachelor's degree in Computer Science, AI, Data Science, or related field
- 5-8 years of experience in AI/ML or Data Science roles
- Proficiency in Python
- Proficiency in SQL
- Experience with Scikit-learn
- Experience with PyTorch or TensorFlow
- Experience building LLM-based/Generative AI solutions, RAG pipelines, and prompt engineering
- Experience with LangChain, LangGraph, and HuggingFace
- Experience building backend APIs with FastAPI, Flask, or Django
- Experience with PostgreSQL and ArangoDB
- Experience with vector databases (FAISS, Milvus, Chroma)
- Experience with data processing and analysis using Pandas and NumPy
- Experience with Matplotlib or Power BI for visualization
- Containerization and deployment experience using Docker
- Experience with Git and Linux development workflows
- Knowledge of RBAC, authentication/authorization (JWT, SSO) and secure coding/data protection practices
- Familiarity with agent-based/multi-agent systems and agent orchestration frameworks (CrewAI etc.)
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The Company
What We Do
Esyasoft is a global energy-transition technology group delivering integrated smart-utility solutions across software, analytics and AI, e-mobility, battery energy storage, and digital infrastructure. Its offerings help utilities, governments, and businesses modernize electricity, water, and gas infrastructure, improve operational visibility, optimize resources, and support cleaner energy systems through smart metering, IoT-driven platforms, intelligent infrastructure, and energy services for connected, sustainable energy networks.
