AI Engineer.

Employment Typ

Compensation

Role Summary

What You’ll Work On

• Multi-Agent Collaboration, Reasoning, Memory & Human Alignment Build intelligent agents and swarms with multi-agent collaboration, reasoning, planning, memory, and alignment with human feedback. Use protocols such as MCP and A2A and frameworks such as LangChain and Crew AI .
• Retrieval-Augmented Generation (RAG) Develop hybrid pipelines using vector databases (FAISS, Qdrant, Pinecone) and transformer-based generation models
• Multimodal AI (Language + Vision + Audio + Video) Build systems that process and combine intelligence across language, vision, audio, and video.
• Predictive Modeling Build predictive pipelines using deep learning architectures (e.g., LSTMs, CNNs, RNNs), transformer-based models (e.g. openAI, LLama, Qwen, Mistral) , and ensemble methods (e.g., XGBoost, LightGBM, Random Forests). Emphasize modeling depth, generalization, interpretability, maintainability and dynamic improvements.
• Conversational Assistants Develop conversational assistants with advanced capabilities such as model based recommendations, user-query based what-if scenario analyses and continuous improvements based on memory and human feedback.
• FastAPI-Based Backend APIs Wrap agents and models into versioned, secure, and production-grade FastAPI microservices .
• Model Lifecycle Management Track, evaluate, and manage model lifecycles using MLflow, DVC, and internal governance tools.
• Data Engineering & Integration Ingest and transform data from SQL and NoSQL (e.g. MongoDB) sources, APIs, and distributed pipelines.
  

Required Skills And Qualifications

• 1–4 years of experience in AI/ML product development or applied data science
• Strong Python skills: Pandas, NumPy, scikit-learn, Transformers, PyTorch/TensorFlow
• Hands-on experience with LLMs (OpenAI, Mistral, Claude, Llama, Deepseek, Gemini, etc.), LangChain, prompt engineering, and integration with real-world use cases
• Proven experience building agentic systems, including reasoning agents and multi-agent collaboration
• Deep expertise in predictive modeling using transformers, deep learning and ensemble methods
• Familiarity with image, audio, and video model development
• Familiarity with model monitoring and re-training
• Exposure to both SQL and NoSQL databases
• Experience building and deploying Python based backend APIs using FastAPI
• Proficiency with Git workflows, CI/CD, and modular code development
• Strong communication, documentation, and architectural thinking
  

Desirable Skills

• Candidates with experience in reinforcement learning (including RLHF) will be preferred.
• Prior exposure to supervised fine-tuning (SFT) and parameter-efficient tuning approaches such as LoRA and QLoRA is desirable.
• Familiarity with workflow orchestration tools like Airflow, Celery, Prefect, or Dagster will be advantageous.
• Experience building autoscaling and serverless architectures using AWS Lambda, ECS, or EKS is a plus.
• Candidates with an expertise in backend engineering with a focus on microservice optimization will be preferred.
• Candidates with experience in containerization and orchestration using Docker and Kubernetes will be valued.
• Hands-on knowledge of designing conversational UI or chatbot pipelines is beneficial.
• Prior experience working with big data tools like Spark, Hive, or Hadoop is desirable.
• Experience deploying AI systems in BFSI, healthcare, e-commerce, or government environments will be an added advantage.
  

What You’ll Gain

• End-to-End Ownership from design to deployment
• Agent-First Ecosystem Exposure
• Product + Custom Work across client and platform needs
• Leadership Track into applied AI architecture
• Live Enterprise Impact across sectors
  

If you think in embeddings, talk in APIs, and believe human-aligned, reasoning-first multi-agent systems are the future—join Galaxy Office Automation and help build them.