Neotechie
https://neotechie.inAbout
Neotechie delivers operational transformation through RPA automation, custom softwareengineering, managed IT services, and enterprise data & AI — for Finance, Healthcare,and Energy organizations that need production-grade results.
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Jobs at Neotechie
PythonPython Engineer – AI/ML & LLM Specialist
Neotechie Private Limited | Noida, India | Working Hours: EST
About Neotechie
Neotechie is a forward-thinking IT consulting and digital transformation company building AI-driven solutions for clients across industries. Our engineering team works at the cutting edge of Generative AI, NLP, and intelligent automation — turning complex language model capabilities into real-world products that deliver measurable business value.
About the Role
We're looking for a hands-on Python Engineer with deep expertise in NLP and Large Language Models to join our AI practice. You'll own the full lifecycle of LLM-powered applications — from architecture and fine-tuning through to production deployment — and work closely with product and data teams to solve real client problems with language AI.
This is a role for a builder who is equally comfortable in a Jupyter notebook and a production environment.
What You'll Do
- Design and build production-grade AI/ML systems with a focus on NLP and LLM-powered applications (chatbots, summarization, classification, entity extraction, semantic search, and more).
- Fine-tune and adapt pre-trained LLMs (e.g., LLaMA, Mistral, GPT variants) for domain-specific use cases using techniques such as LoRA, PEFT, and RLHF.
- Architect and implement RAG (Retrieval-Augmented Generation) pipelines integrating vector databases (e.g., Pinecone, Weaviate, ChromaDB) with LLM backends.
- Build robust, end-to-end ML pipelines covering data preprocessing, model training, evaluation, versioning, and deployment.
- Optimize models for inference efficiency — including quantization, pruning, and batching strategies — to meet production latency and cost requirements.
- Develop and maintain REST APIs and microservices to expose AI capabilities to downstream applications.
- Collaborate with product, data, and engineering teams to translate business requirements into scalable AI solutions.
- Monitor deployed models, manage retraining cycles, and maintain experiment tracking using MLOps best practices.
- Stay current with the fast-moving LLM landscape and bring new techniques and tools into the team's practice.
What You Bring
Required
- 5+ years of professional Python development experience, with at least 2–3 years focused on AI/ML or LLM-driven applications in production.
- Hands-on experience with PyTorch for model development, training, and optimization.
- 3+ years building NLP solutions — text classification, summarization, NER, conversational AI, semantic similarity, or related tasks.
- Solid experience with Hugging Face Transformers, LangChain, LlamaIndex, or equivalent LLM orchestration frameworks.
- Demonstrated experience fine-tuning transformer-based models and working with prompt engineering techniques (few-shot, chain-of-thought, system prompting).
- Strong SQL and data manipulation skills; proficiency with Pandas, NumPy, and similar libraries.
- Experience deploying ML models via APIs (FastAPI/Flask) and managing model lifecycle in production (monitoring, versioning, retraining).
- Solid understanding of data structures, algorithms, and model evaluation methodologies.
Preferred
- Experience building RAG pipelines and working with vector databases (Pinecone, Weaviate, ChromaDB, FAISS).
- Familiarity with cloud platforms — AWS SageMaker, GCP Vertex AI, or Azure ML.
- Exposure to MLOps tooling: MLflow, Weights & Biases, DVC, or similar.
- Experience with distributed training frameworks or large-scale data processing (Spark, Ray, Dask).
- Knowledge of model compression techniques: quantization, distillation, pruning for inference optimization.
- Contributions to open-source AI/ML projects are a strong plus.
Education
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Equivalent experience through projects, open-source contributions, or self-directed learning is fully considered.
Why Neotechie
- Work on real GenAI products — not proofs of concept.
- Direct exposure to LLM architecture decisions, not just integration work.
- Fast-moving team with autonomy and minimal bureaucracy.
- Growth path into AI architecture or technical leadership.
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