Job Description & How to Apply Below
About Pocket FM
Pocket FM is a leading audio entertainment platform focused on immersive, long-form audio storytelling. The platform offers episodic audio series across genres such as romance, drama, thriller, and fantasy. Pocket FM follows a mobile-first approach, enabling users to listen anytime and anywhere. Founded in India, the company has expanded rapidly across global markets, including the US.It supports multiple regional and international languages to reach a diverse audience.
Pocket FM empowers creators through a strong content and monetization ecosystem
About the Role
We are seeking a talented AI & Data Engineer to join our team. In this role, you will design, build, and maintain robust data pipelines while developing and deploying cutting-edge AI solutions. You will work at the intersection of data engineering and artificial intelligence, leveraging modern cloud platforms and AI frameworks to drive business value.
Responsibilities
Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, Apache Spark, and cloud-native services
Build and maintain data lakehouse architectures leveraging Delta Lake and Databricks Unity Catalog
Develop AI-powered applications using agentic frameworks such as Lang Chain, Llama Index, Auto Gen, or CrewAI
Fine-tune and deploy Large Language Models (LLMs) for domain-specific use cases using techniques like LoRA, QLoRA, and PEFT
Implement RAG (Retrieval-Augmented Generation) systems for enterprise knowledge management
Create and manage vector databases for semantic search and embedding storage
Collaborate with data scientists and ML engineers to product ionize machine learning models
Ensure data quality, governance, and security across all pipelines and AI systems
Monitor, troubleshoot, and optimize data infrastructure for performance and cost efficiency
Document technical designs, processes, and best practices for knowledge sharing
Qualifications
3-5 years of hands-on experience in data engineering, AI/ML engineering, or related roles
Strong proficiency with Databricks platform including Delta Lake, MLflow, and Databricks SQL
Expert-level knowledge of ETL/ELT processes, data modeling, and pipeline orchestration
Experience with AI agentic frameworks (Lang Chain, Llama Index, Auto Gen, Semantic Kernel)
Hands-on experience with LLMs including GPT-4, Claude, Llama, Mistral, or similar models
Practical knowledge of fine-tuning techniques:
LoRA, QLoRA, PEFT, and full fine-tuning approaches
Proficiency in Python and SQL; familiarity with Scala is a plus
Experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML services
Understanding of vector databases (Pinecone, Weaviate, Chroma, Milvus)
Strong foundation in software engineering principles and version control (Git)
Required Skills
Experience with prompt engineering and LLM evaluation frameworks
Knowledge of MLOps practices and tools (Kubeflow, MLflow, Weights & Biases)
Familiarity with streaming data technologies (Kafka, Spark Streaming)
Experience with containerization (Docker) and orchestration (Kubernetes)
Background in NLP, computer vision, or other AI domains
Relevant certifications (Databricks, AWS, Azure, or GCP)
Technology Stack
Data Platform:
Databricks, Delta Lake, Apache Spark, Unity Catalog
AI/ML:
Lang Chain, Llama Index, Hugging Face, PyTorch, OpenAI API, Anthropic API
Cloud: AWS/Azure/GCP, Terraform, CI/CD pipelines
Languages:
Python, SQL, Py Spark
Tools:
Git, Docker, MLflow, Airflow/Prefect
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