The Opportunity:
TSS Consultancy PVT LTD is seeking an experienced and dynamic Lead Software Engineer – AI to spearhead our cross-functional AI engineering team. In this pivotal role, you will be responsible for leading the development and deployment of cutting-edge AI-powered solutions across various domains, including computer vision, natural language processing (NLP), and predictive modeling. You will architect scalable AI systems, champion the adoption of MLOps best practices, and provide guidance and mentorship to a talented team of engineers. This is a full-time position based in one of our key technology centers: Mumbai, Rajkot, or Ahmedabad, and you will report directly to the Head of Engineering / CTO.
Key Responsibilities:
AI Product Development:
- Lead the end-to-end lifecycle of AI-powered solutions, encompassing computer vision, NLP, and predictive modeling.
- Drive the development of innovative use cases in areas such as image enhancement, document intelligence, entity extraction, and automated data validation.
- Apply advanced deep learning techniques, transformer architectures, and foundation model fine-tuning to build scalable solutions for real-world applications.
- Design and architect robust and scalable AI services, seamlessly integrating them via REST APIs and microservices.
- Proactively explore and evaluate cutting-edge technologies in the AI landscape, including generative AI, multilingual AI, and biometric verification, to identify potential applications.
MLOps & AI Engineering:
- Build and maintain robust CI/CD pipelines specifically tailored for model training, deployment, and rigorous testing.
- Implement comprehensive automation for model monitoring, retraining workflows, and ensure end-to-end traceability for both models and datasets.
- Leverage industry-standard MLOps tools such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes to facilitate production-grade deployment and management of AI systems.
NLP & Language Model Applications:
- Lead the research, development, and proof-of-concept (PoC) efforts for domain-specific language models.
- Implement state-of-the-art transformer-based architectures to address complex NLP tasks, including text summarization, semantic search, and question-answering (QA) systems.
- Expertly fine-tune large language models (LLMs) to extract valuable insights from unstructured data sources.
- Strategically integrate NLP modules into core business workflows and real-time operational systems.
Engineering Leadership:
- Provide effective mentorship and guidance to a team of skilled machine learning engineers and software developers, fostering their professional growth.
- Collaborate closely with cross-functional stakeholders, including product managers and business analysts, to define product vision and contribute to the engineering roadmap.
- Take ownership of sprint planning, ensure timely and high-quality delivery of AI solutions, and conduct thorough code and architecture reviews.
What We're Looking For:
Required Technical Skills:
- A minimum of 5 years of hands-on experience in AI/ML development, with demonstrable expertise in:
- Computer Vision: (e.g., Optical Character Recognition (OCR), Deepfake Detection)
- Natural Language Processing (NLP): (e.g., Text Parsing, Transformer Networks, GPT fine-tuning)
- Machine Learning Frameworks: PyTorch, TensorFlow, Hugging Face Transformers library
- MLOps Tools: MLflow, Kubeflow, Airflow, Docker, Kubernetes
- Strong proficiency in Python programming; familiarity with Java, Go, or Node.js is considered a plus.
- Solid understanding and practical experience with RESTful APIs and microservices architecture.
- Proven experience with DevOps tools and practices, including Jenkins, GitHub Actions, Prometheus, and Grafana.
Leadership Skills:
- Demonstrated success in leading and managing teams of 5 to 10 engineers, fostering a collaborative and high-performing environment.
- Excellent communication skills, both written and verbal, with the ability to effectively convey complex technical concepts to both technical and non-technical audiences.
- Strong project management skills, with a track record of delivering AI projects on time and within scope.
- The ability to translate abstract product requirements into well-defined and scalable AI system architectures.