Senior AI/ML Engineer (Traditional ML + Generative AI)
Budget: $2 – $8 USD
## About the Role
We are looking for an experienced AI/ML Engineer who combines a strong foundation in traditional Machine Learning and Data Science with hands-on expertise in modern Generative AI systems.
This role requires someone who can build production-ready ML solutions, develop intelligent LLM applications, design multi-agent systems, implement Retrieval-Augmented Generation (RAG), and deploy scalable AI services using modern MLOps practices.
You'll work across the full AI lifecycle—from data preparation and model development to deployment, monitoring, and continuous improvement.
---
## Responsibilities
* Design, build, and deploy end-to-end machine learning solutions.
* Develop predictive models using classical ML algorithms for structured and unstructured data.
* Build production-grade LLM applications using frameworks such as LangGraph, LangChain, CrewAI, or similar.
* Design and implement multi-agent AI systems with planning, orchestration, memory, and tool usage.
* Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and hybrid search.
* Optimize prompts, retrieval quality, and agent workflows for accuracy, latency, and cost.
* Develop scalable APIs and backend services for AI applications.
* Build evaluation pipelines for LLMs and RAG systems, including automated testing and monitoring.
* Collaborate with product managers, software engineers, and data engineers to deliver AI-powered products.
* Deploy, monitor, and maintain ML/LLM workloads in production using modern MLOps practices.
---
## Required Qualifications
### Traditional Machine Learning & Data Science
* 5+ years of experience building production ML systems.
* Strong understanding of:
* Supervised and unsupervised learning
* Classification and regression
* Clustering
* Recommendation systems
* Feature engineering
* Model evaluation and validation
* Time series forecasting (preferred)
* Statistical analysis and experimentation
* Experience with:
* Python
* Pandas
* NumPy
* Scikit-learn
* XGBoost / LightGBM / CatBoost
* Strong SQL skills and experience working with large datasets.
### Generative AI
Hands-on experience with:
* Large Language Models (OpenAI, Claude, Gemini, Llama, etc.)
* Multi-agent architectures
* RAG systems
* Prompt engineering
* Tool calling / Function calling
* Structured outputs
* Context management
* Memory architectures
* Vector databases such as Pinecone, Qdrant, Weaviate, Milvus, Chroma, or FAISS
* Embeddings and semantic search
* Hybrid retrieval and reranking
Experience with one or more frameworks:
* LangGraph
* LangChain
* CrewAI
* Google ADK
* Semantic Kernel
* AutoGen
* LlamaIndex
---
## MLOps & Production Engineering
Experience with:
* Docker
* Kubernetes
* CI/CD pipelines
* MLflow
* Model versioning
* Model deployment
* Model monitoring
* Experiment tracking
* Feature stores
* GPU inference optimization
* REST APIs (FastAPI preferred)
Cloud experience with at least one platform:
* AWS
* Google Cloud Platform
* Microsoft Azure
---
## Nice to Have
* Deep Learning (PyTorch or TensorFlow)
* Fine-tuning LLMs (LoRA/QLoRA/PEFT)
* Distributed training
* Knowledge Graphs / GraphRAG
* Computer Vision or NLP experience
* Reinforcement Learning
* Streaming data pipelines (Kafka, Pub/Sub)
* Airflow or similar orchestration tools
* Experience building AI agents for enterprise applications
---
## What We're Looking For
The ideal candidate is:
* Strong in both classical Machine Learning and modern Generative AI
* Comfortable owning projects from research through production
* Experienced building scalable AI systems rather than prototypes
* Passionate about solving complex engineering problems
* Able to work independently while collaborating across cross-functional teams
* Focused on writing clean, maintainable, production-quality code
---
## Preferred Tech Stack
* Python
* FastAPI
* Scikit-learn
* PyTorch / TensorFlow
* LangGraph / LangChain
* OpenAI / Claude / Gemini
* Pinecone / Qdrant / Weaviate
* PostgreSQL
* Redis
* Docker
* Kubernetes
* MLflow
* GitHub Actions
* AWS / GCP / Azure
---
## Why Join Us?
* Build cutting-edge AI products used in production.
* Work on both traditional ML and next-generation AI systems.
* Solve challenging real-world problems using the latest AI technologies.
* Collaborate with experienced engineers in a fast-paced, innovation-driven environment.
* Opportunity to shape the architecture of modern AI platforms from the ground up.
We are looking for an experienced AI/ML Engineer who combines a strong foundation in traditional Machine Learning and Data Science with hands-on expertise in modern Generative AI systems.
This role requires someone who can build production-ready ML solutions, develop intelligent LLM applications, design multi-agent systems, implement Retrieval-Augmented Generation (RAG), and deploy scalable AI services using modern MLOps practices.
You'll work across the full AI lifecycle—from data preparation and model development to deployment, monitoring, and continuous improvement.
---
## Responsibilities
* Design, build, and deploy end-to-end machine learning solutions.
* Develop predictive models using classical ML algorithms for structured and unstructured data.
* Build production-grade LLM applications using frameworks such as LangGraph, LangChain, CrewAI, or similar.
* Design and implement multi-agent AI systems with planning, orchestration, memory, and tool usage.
* Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and hybrid search.
* Optimize prompts, retrieval quality, and agent workflows for accuracy, latency, and cost.
* Develop scalable APIs and backend services for AI applications.
* Build evaluation pipelines for LLMs and RAG systems, including automated testing and monitoring.
* Collaborate with product managers, software engineers, and data engineers to deliver AI-powered products.
* Deploy, monitor, and maintain ML/LLM workloads in production using modern MLOps practices.
---
## Required Qualifications
### Traditional Machine Learning & Data Science
* 5+ years of experience building production ML systems.
* Strong understanding of:
* Supervised and unsupervised learning
* Classification and regression
* Clustering
* Recommendation systems
* Feature engineering
* Model evaluation and validation
* Time series forecasting (preferred)
* Statistical analysis and experimentation
* Experience with:
* Python
* Pandas
* NumPy
* Scikit-learn
* XGBoost / LightGBM / CatBoost
* Strong SQL skills and experience working with large datasets.
### Generative AI
Hands-on experience with:
* Large Language Models (OpenAI, Claude, Gemini, Llama, etc.)
* Multi-agent architectures
* RAG systems
* Prompt engineering
* Tool calling / Function calling
* Structured outputs
* Context management
* Memory architectures
* Vector databases such as Pinecone, Qdrant, Weaviate, Milvus, Chroma, or FAISS
* Embeddings and semantic search
* Hybrid retrieval and reranking
Experience with one or more frameworks:
* LangGraph
* LangChain
* CrewAI
* Google ADK
* Semantic Kernel
* AutoGen
* LlamaIndex
---
## MLOps & Production Engineering
Experience with:
* Docker
* Kubernetes
* CI/CD pipelines
* MLflow
* Model versioning
* Model deployment
* Model monitoring
* Experiment tracking
* Feature stores
* GPU inference optimization
* REST APIs (FastAPI preferred)
Cloud experience with at least one platform:
* AWS
* Google Cloud Platform
* Microsoft Azure
---
## Nice to Have
* Deep Learning (PyTorch or TensorFlow)
* Fine-tuning LLMs (LoRA/QLoRA/PEFT)
* Distributed training
* Knowledge Graphs / GraphRAG
* Computer Vision or NLP experience
* Reinforcement Learning
* Streaming data pipelines (Kafka, Pub/Sub)
* Airflow or similar orchestration tools
* Experience building AI agents for enterprise applications
---
## What We're Looking For
The ideal candidate is:
* Strong in both classical Machine Learning and modern Generative AI
* Comfortable owning projects from research through production
* Experienced building scalable AI systems rather than prototypes
* Passionate about solving complex engineering problems
* Able to work independently while collaborating across cross-functional teams
* Focused on writing clean, maintainable, production-quality code
---
## Preferred Tech Stack
* Python
* FastAPI
* Scikit-learn
* PyTorch / TensorFlow
* LangGraph / LangChain
* OpenAI / Claude / Gemini
* Pinecone / Qdrant / Weaviate
* PostgreSQL
* Redis
* Docker
* Kubernetes
* MLflow
* GitHub Actions
* AWS / GCP / Azure
---
## Why Join Us?
* Build cutting-edge AI products used in production.
* Work on both traditional ML and next-generation AI systems.
* Solve challenging real-world problems using the latest AI technologies.
* Collaborate with experienced engineers in a fast-paced, innovation-driven environment.
* Opportunity to shape the architecture of modern AI platforms from the ground up.