AIML Engineer Speech, RAG & Fine-Tuning

Rawalpindi - Pakistan

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Title: AI/ML Engineer Speech RAG & Fine-Tuning

Location: Bahria Town Phase 7 Rawalpindi
Employment Type: Full-timeOnsite (10AM - 7PM)

Job Description:

We are seeking a highly skilled AI/ML Engineer with expertise in speech-to-speech pipelines open-source models and LLM fine-tuning. The ideal candidate will work on designing developing and deploying cutting-edge speech and language AI solutions integrating open-source frameworks with advanced fine-tuning methods to deliver production-ready systems.

Key Responsibilities:

Design and implement speech-to-speech pipelines using open-source models (Whisper Wav2Vec etc.).
Develop and optimize speech-to-text (STT) and text-to-speech (TTS) systems leveraging Coqui or similar frameworks.
Work with large language models (LLMs) such as LLaMA 2 LLaMA 3 for NLP applications.
Apply LoRA and PEFT-based fine-tuning techniques to customize LLMs for domain-specific tasks.
Build and optimize Retrieval-Augmented Generation (RAG)-based systems for knowledge-grounded responses.
Develop and integrate agentic AI systems with reasoning and task automation capabilities.
Collaborate with cross-functional teams (data engineers product managers software developers) to deliver scalable AI solutions.
Monitor evaluate and optimize deployed AI models for accuracy latency and efficiency.

Requirements:

Strong experience in AI/ML model development with open-source speech and language models.
Hands-on experience with Whisper Wav2Vec Coqui TTS/STT frameworks.
Proven track record with LLaMA 2 LLaMA 3 or similar LLMs.
Proficiency in fine-tuning techniques: LoRA PEFT and parameter-efficient training.
Experience in RAG-based systems for knowledge retrieval and contextual response generation.
Familiarity with agentic AI frameworks for building task-oriented agents.
Strong programming skills in Python PyTorch TensorFlow.
Experience with Hugging Face LangChain and vector databases (FAISS Pinecone Weaviate etc.).
Knowledge of cloud platforms (AWS GCP Azure) and containerization (Docker Kubernetes).
Strong problem-solving skills and ability to optimize model performance.

Preferred Qualifications:

Masters or PhD in Computer Science AI/ML Data Science or related field.
Publications or projects in speech AI LLM fine-tuning or agentic AI.
Experience with distributed training and model deployment at scale.

What We Offer:

Lunch provided by the company
Medical Allowance
Competitive compensation and growth opportunities.
Opportunity to work with state-of-the-art open-source AI models.
Collaborative environment with AI researchers and engineers.

Job Title: AI/ML Engineer Speech RAG & Fine-Tuning Location: Bahria Town Phase 7 RawalpindiEmployment Type: Full-timeOnsite (10AM - 7PM) Job Description: We are seeking a highly skilled AI/ML Engineer with expertise in speech-to-speech pipelines open-source models and LLM fine-tuning. The ideal cand...

Job Title: AI/ML Engineer Speech RAG & Fine-Tuning

Location: Bahria Town Phase 7 Rawalpindi
Employment Type: Full-timeOnsite (10AM - 7PM)

Job Description:

Key Responsibilities:

Design and implement speech-to-speech pipelines using open-source models (Whisper Wav2Vec etc.).
Develop and optimize speech-to-text (STT) and text-to-speech (TTS) systems leveraging Coqui or similar frameworks.
Work with large language models (LLMs) such as LLaMA 2 LLaMA 3 for NLP applications.
Apply LoRA and PEFT-based fine-tuning techniques to customize LLMs for domain-specific tasks.
Build and optimize Retrieval-Augmented Generation (RAG)-based systems for knowledge-grounded responses.
Develop and integrate agentic AI systems with reasoning and task automation capabilities.
Collaborate with cross-functional teams (data engineers product managers software developers) to deliver scalable AI solutions.
Monitor evaluate and optimize deployed AI models for accuracy latency and efficiency.

Requirements:

Strong experience in AI/ML model development with open-source speech and language models.
Hands-on experience with Whisper Wav2Vec Coqui TTS/STT frameworks.
Proven track record with LLaMA 2 LLaMA 3 or similar LLMs.
Proficiency in fine-tuning techniques: LoRA PEFT and parameter-efficient training.
Experience in RAG-based systems for knowledge retrieval and contextual response generation.
Familiarity with agentic AI frameworks for building task-oriented agents.
Strong programming skills in Python PyTorch TensorFlow.
Experience with Hugging Face LangChain and vector databases (FAISS Pinecone Weaviate etc.).
Knowledge of cloud platforms (AWS GCP Azure) and containerization (Docker Kubernetes).
Strong problem-solving skills and ability to optimize model performance.