Al/ML Engineer
Overview
We are looking for a hands-on AI/ML Engineer to build and deploy intelligent AI solutions across a communication platform. The role involves developing LLM-powered applications, voice AI capabilities, NLP pipelines, and production-grade ML systems that enable automation and real-time decision-making.
What You'll Do10
- 1Build and deploy AI/ML solutions using Large Language Models (LLMs), NLP, speech AI, and generative AI technologies.
- 2Develop LLM-powered features including summarization, sentiment analysis, intent detection, auto-disposition, escalation tagging, and agent assistance.
- 3Build and implement Agentic AI workflows, prompt pipelines, and retrieval-based systems using frameworks like LangChain.
- 4Work with speech technologies including STT, TTS, Whisper, and voice intelligence solutions.
- 5Fine-tune, evaluate, and optimize AI models for accuracy, latency, and scalability.
- 6Build AI inference services and APIs using Python, FastAPI, Docker, and cloud-native technologies.
- 7Develop and maintain ML pipelines for data processing, model training, evaluation, and continuous improvement.
- 8Integrate AI models into product workflows such as communication platforms, CRM systems, dialers, and automation solutions.
- 9Optimize AI systems for high-volume production environments with focus on performance and reliability.
- 10Work with data technologies such as PostgreSQL, Redis, Kafka, and build scalable data workflows.
Requirements10
- 14–7 years of experience in AI/ML engineering with production-grade AI applications.
- 2Strong hands-on experience with LLMs, Generative AI, NLP, and conversational AI systems.
- 3Experience building Agentic AI applications and voice bot solutions is highly preferred.
- 4Proficiency in Python and experience developing AI services using FastAPI or similar frameworks.
- 5Hands-on experience with frameworks/models such as LangChain, HuggingFace, Whisper, GPT, or equivalent.
- 6Strong understanding of NLP concepts including text classification, summarization, sentiment analysis, speech processing, and emotion detection.
- 7Experience deploying ML solutions using Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
- 8Familiarity with databases and messaging systems such as PostgreSQL, Redis, Kafka, or RabbitMQ.
- 9Exposure to Rasa, Coqui TTS, speech emotion recognition, or conversational intelligence platforms is an advantage.
- 10Prior experience in SaaS, contact center, CRM, dialer, or customer communication platforms is preferred.
Who Should Apply
The ideal candidate is a hands-on AI/ML Engineer with 4–7 years of experience building production-grade AI applications. They possess deep expertise in LLMs, Generative AI, NLP, and conversational AI systems, and are proficient in Python and frameworks like FastAPI. Experience with Agentic AI and voice bot solutions is highly preferred, along with a strong individual ownership mindset to thrive in a fast-paced startup environment.
Location
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Application Tip
Highlight your experience with LLM-powered applications and voice AI technologies in your resume and cover letter, and provide examples of end-to-end AI solutions you have deployed in production.
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1Application received
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2ATS + eligibility screening
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