Generative AI Engineer

0 years

0 Lacs

Posted:8 hours ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

About MAKO:


Mako IT Lab


culture


We don’t just build long-term partnerships with clients—we build long-term careers for our people. At Mako, you’ll be part of a collaborative, supportive, and fast-growing global team where curiosity is encouraged, initiative is celebrated, and every individual plays a meaningful role in shaping the company’s journey.



Role Overview:


AI Engineer



Key Responsibilities:


1. LLM, VLLM & Agentic System Development

·      Build autonomous LLM agents using LangChain, LangGraph, and FastMCP.

·      Develop RAG workflows using embeddings, vector stores, and knowledge-grounded reasoning.

VLLM / SGLang / other high-throughput inference backends

·      Implement Tavily web-search integrations for real-time knowledge augmentation.

·      Optimize inference using quantized GGUF, tensorized formats, and GPU-accelerated pipelines.

2. Multimodal & Image Generation Systems

Stable Diffusion

·      Integrate LoRAs, control modules, and diffusion-based fine-tuning for custom domains.

·      Develop multimodal agents that combine LLM reasoning with vision tasks such as classification, captioning, or image prompts.

3. Backend & Infrastructure Engineering

·      Build robust FastAPI services for orchestrating LLMs, Stable Diffusion, retrieval, and agentic tasks.

Kafka

·      Implement auditing, agent-output monitoring, and API-layer logging for end-to-end traceability.

4. High-level API & Third-party Integrations

·      Integrate third-party services: authentication, analytics, search APIs, cloud inference APIs, and enterprise data sources.

·      Build secure and scalable API layers for production deployments.

5. Fine-tuning & Model Lifecycle Management

·      Fine-tune LLaMA, Mistral, Phi-3, and diffusion models for domain-specific tasks.

·      Use MLflow for tracking experiments, hyperparameters, metrics, and versioning.

·      Conduct evaluation on hallucinations, retrieval consistency, reasoning depth, and multimodal accuracy.




Required Skills & Qualifications:


Core AI/LLM Skills

LLMs

VLLM

·      Model quantization (GGUF), optimization, and GPU memory tuning

·      Agent frameworks & tool calling (FastMCP, Groq, Hugging Face)

Multimodal & Image Generation

·      Stable Diffusion, ControlNet, LoRA fine-tuning, custom pipelines

·      Diffusers, ComfyUI, or InvokeAI experience (bonus)

Engineering & Systems

·      Kafka-based event-driven systems

·      FastAPI/Flask/Node.js backend development

·      Third-party API integrations

·      Docker, CI/CD, and cloud platforms (GCP/Azure)

Databases & Retrieval

·      MongoDB, DuckDB,

·      Embedding stores, vector databases (Pinecone / Qdrant), retrieval optimization

Observability & MLOps

·      MLflow for experiment tracking and model lifecycle

·      Performance monitoring, logging, auditing, API observability

Frontend (Good to have)

·      React, Redux, Next.js, Electron.js for dashboards and AI interfaces

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