Machine Learning Engineer

Job description

Job Title: ML Engineer

Company

Location

Type

Experience

Level

About Darwix AI

Darwix AI

We serve leading enterprise clients across India, the UAE, and Southeast Asia and are backed by global VCs, top operators from Google, Salesforce, and McKinsey, and CXOs from the industry.

This is your opportunity to join a high-caliber founding tech team solving frontier problems in real-time voice AI, multilingual transcription, retrieval-augmented generation (RAG), and fine-tuned LLMs at scale.

Role Overview

ML Engineer

You will lead a team of AI engineers and work closely with product managers, software architects, and data teams to ensure technical excellence, scalable architecture, and rapid iteration cycles.

This is a high-ownership, hands-on leadership role where you will code, architect, and lead simultaneously.

Key Responsibilities

AI Architecture & Model Development

• Architect end-to-end AI pipelines for transcription, real-time inference, LLM integration, and vector-based retrieval.
• Build, fine-tune, and deploy STT models (Whisper, Wav2Vec2.0) and diarization systems for speaker separation.
• Implement GenAI pipelines using OpenAI, Gemini, LLaMA, Mistral, and other LLM APIs or open-source models.

Real-Time Voice AI System Development

• Design low-latency pipelines for capturing and processing audio in real-time across multi-lingual environments.
• Work on WebSocket-based bi-directional audio streaming, chunked inference, and result caching.
• Develop asynchronous, event-driven architectures for voice processing and decision-making.

RAG & Knowledge Graph Pipelines

• Create retrieval-augmented generation (RAG) systems that pull from structured and unstructured knowledge bases.
• Build vector DB architectures (e.g., FAISS, Pinecone, Weaviate) and connect to LangChain/LlamaIndex workflows.
• Own chunking, indexing, and embedding strategies (OpenAI, Cohere, Hugging Face embeddings).

Fine-Tuning & Prompt Engineering

• Fine-tune LLMs and foundational models using RLHF, SFT, PEFT (e.g., LoRA) as needed.
• Optimize prompts for summarization, categorization, tone analysis, objection handling, etc.
• Perform few-shot and zero-shot evaluations for quality benchmarking.

Pipeline Optimization & MLOps

• Ensure high availability and robustness of AI pipelines using CI/CD tools, Docker, Kubernetes, and GitHub Actions.
• Work with data engineering to streamline data ingestion, labeling, augmentation, and evaluation.
• Build internal tools to benchmark latency, accuracy, and relevance for production-grade AI features.

Team Leadership & Cross-Functional Collaboration

• Lead, mentor, and grow a high-performing AI engineering team.
• Collaborate with backend, frontend, and product teams to build scalable production systems.
• Participate in architectural and design decisions across AI, backend, and data workflows.

Key Technologies & Tools

Languages & Frameworks

Python, FastAPI, Flask, LangChain, PyTorch, TensorFlow, HuggingFace Transformers

Voice & Audio

Whisper, Wav2Vec2.0, DeepSpeech, pyannote.audio, AssemblyAI, Kaldi, Mozilla TTS

Vector DBs & RAG

FAISS, Pinecone, Weaviate, ChromaDB, LlamaIndex, LangGraph

LLMs & GenAI APIs

OpenAI GPT-4/3.5, Gemini, Claude, Mistral, Meta LLaMA 2/3

DevOps & Deployment

Docker, GitHub Actions, CI/CD, Redis, Kafka, Kubernetes, AWS (EC2, Lambda, S3)

Databases

MongoDB, Postgres, MySQL, Pinecone, TimescaleDB

Monitoring & Logging

Prometheus, Grafana, Sentry, Elastic Stack (ELK)

Requirements & Qualifications

• 2-6 years of experience in building and deploying AI/ML systems, with at least 2+ years in NLP or voice technologies.
• Proven track record of production deployment of ASR, STT, NLP, or GenAI models.
• Hands-on experience building systems involving vector databases, real-time pipelines, or LLM integrations.

Educational Background

• Bachelor's or Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Tier 1 institute preferred (IITs, BITS, IIITs, NITs, or global top 100 universities).

Technical Skills

• Strong coding experience in Python and familiarity with FastAPI/Django.
• Understanding of distributed architectures, memory management, and latency optimization.
• Familiarity with transformer-based model architectures, training techniques, and data pipeline design.

Bonus Experience

• Worked on multilingual speech recognition and translation.
• Experience deploying AI models on edge devices or browsers.
• Built or contributed to open-source ML/NLP projects.
• Published papers or patents in voice, NLP, or deep learning domains.

What Success Looks Like in 6 Months

• Lead the deployment of a real-time STT + diarization system for at least 1 enterprise client.
• Deliver high-accuracy nudge generation pipeline using RAG and summarization models.
• Build an in-house knowledge indexing + vector DB framework integrated into the product.
• Mentor 2–3 AI engineers and own execution across multiple modules.
• Achieve <1 sec latency on real-time voice-to-nudge pipeline from capture to recommendation.

What We Offer

• Compensation

  : Competitive fixed salary + equity + performance-based bonuses

• Impact

  : Ownership of key AI modules powering thousands of live enterprise conversations

• Learning

  : Access to high-compute GPUs, API credits, research tools, and conference sponsorships

• Culture

  : High-trust, outcome-first environment that celebrates execution and learning

• Mentorship

  : Work directly with founders, ex-Microsoft, IIT-IIM-BITS alums, and top AI engineers

• Scale

  : Opportunity to scale an AI product from 10 clients to 100+ globally within 12 months

⚠️ This Role is NOT for Everyone

🚫 If you're looking for a slow, abstract research role—this is NOT for you.

 🚫 If you're used to months of ideation before shipping—you won't enjoy our speed.

 🚫 If you're not comfortable being hands-on and diving into scrappy builds—you may struggle.

builder

How to Apply

“Why AI at Darwix?”

careers@cur8.in / vishnu.sethi@cur8.in

Application – ML Engineer – [Your Name]

Include links to:

• Any relevant open-source contributions
• LLM/STT models you've fine-tuned or deployed
• RAG pipelines you've worked on

Final Thought

build the world’s most scalable AI sales intelligence platform