Artificial Intelligence Engineer

AI Engineer Machine Learning, Generative & Applied Systems

Location:

About the Role

AI-driven systems

research, experimentation, and production engineering

classical ML

You’ll collaborate closely with product, data, and engineering teams to design, build, and deploy AI systems that learn, reason, and interact naturally with humans.

Who You Are

AI builder

the math and the machinery

design, scale, and productize intelligence

Key Focus Areas (Choose Your Track)

four interconnected disciplines of AI Engineering

Category

You’ll Excel Here If You...

Example Projects You’ll Build

1 Core Machine Learning

Love solving structured data problems, building predictive models, and applying statistical learning.

Predictive analytics, feature pipelines, anomaly detection, customer scoring, explainable models.

2 Deep Learning & Neural Networks

Are passionate about designing architectures and optimizing model performance on GPUs.

CNNs for vision tasks, Transformers for embeddings, model pruning/quantization, distributed training.

3 NLP, LLMs & Conversational AI

Thrive on designing AI that can read, reason, and respond — you understand prompt design, context windows, and retrieval logic.

Fine-tuning GPT/LLaMA, building LangChain/RAG apps, designing multi-turn dialogue flows, LLM integration with APIs.

4 Applied AI – Voice, Vision & Agentic Systems

Enjoy working on end-to-end AI systems that interact with the world — from voicebots to autonomous multi-agent pipelines.

Real-time STTLLMTTS systems, visual reasoning (YOLO/OpenCV), multi-modal AI assistants, LangGraph agents.

Your Core Responsibilities

Depending on your background, you will:

• Design, build, and train

  ML/LLM models tailored to business or product use-cases

• Develop and fine-tune pipelines

  — from data ingestion to model deployment

• Experiment with open-source models

  (LLaMA, Mistral, Falcon, Claude, etc.) and adapt them to specific tasks

• Build multi-agent AI systems

  that combine reasoning, memory, and tool use

• Integrate Speech/Vision AI

  — build real-time voicebots, text understanding, or object detection systems

• Deploy scalable AI systems

  with proper monitoring, versioning, and latency management

• Collaborate cross-functionally

  with data engineers, software developers, and designers to make AI usable, measurable, and explainable

Our Tech Ecosystem

Area

Frameworks / Tools

Languages & Frameworks

Python, C++, JavaScript, FastAPI, Flask

Core ML & Deep Learning

PyTorch, TensorFlow, Scikit-learn, XGBoost, Keras, LightGBM

LLMs & GenAI

LangChain, LangGraph, HuggingFace, CrewAI, LLaMA, GPT, Mistral, Falcon

Speech & Voice AI

Whisper, Deepgram, NeMo, ElevenLabs, Coqui, AWS Polly

Computer Vision

YOLOv8, OpenCV, ViT, SAM, Detectron2, GR00T, Stable Diffusion

Vector Databases & Retrieval

FAISS, Pinecone, ChromaDB, Redis

Infrastructure & MLOps

AWS, GCP, Azure, MLflow, Docker, Kubernetes, Weights & Biases, GitHub Actions

You’ll Be Great Here If You...

• Think like a

  builder

  — you’re not afraid of messy data or incomplete models
• Understand the

  trade-offs

  between open-source vs. proprietary models
• Can

  design for scale

  — low latency, distributed training, multi-agent orchestration
• Write

  clean, modular, production-quality code

• Are comfortable learning, experimenting, and documenting
• Believe that AI is not just about intelligence, but

  impact

  — measurable, human-centered outcomes

Our Ideal Candidate Has

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field
• 3+ years of Project experience in AI/ML development (ML Engineer, AI Engineer, or Research role)
• Hands-on experience with

  LLMs, neural networks, or applied AI systems

• Prior exposure to

  cloud deployment

  and

  vector database usage

• Familiarity with

  evaluation metrics, model optimization, and inference design

Why Join Us

• Build

  AI that ships

  — not research stuck in slides
• Work across

  LLMs, speech, and vision

  , not just one domain
• Collaborate with industry leaders and applied researchers
• Experiment with the

  latest open-source and frontier AI models

• Opportunity to

  own full AI pipelines

  — from data to product delivery
• Flexible work structure, strong mentorship, and clear growth path toward

  Senior / Principal AI Engineer

  roles