Data Scientist / AI Engineer / Machine Learning Engineer

Company Overview:

Galaxy Office Automation Pvt. Ltd. is a trusted enterprise technology partner with 30+ years of experience in delivering secure, large-scale IT systems across India’s top enterprises and government institutions. As we scale into the next frontier of AI-driven intelligence, we’re building a new stack of AI-native services, products, agents, swarms, and conversational assistants that bring powerful, modular intelligence to real-world enterprise workflows – while evolving towards an AI-Factory model to enable scalable, reusable, and composable intelligence across use cases.

Our goal is not buzzwords—it’s capability. We focus on deploying reliable, composable, and production-grade AI systems that can plug directly into existing ecosystems and deliver immediate business value.

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.