we are looking for an experienced and results-driven ai developer to join our team. the ideal candidate will be responsible for developing, deploying, and optimizing ai and machine learning models to solve real-world business problems. you will collaborate with cross-functional teams to deliver scalable and ethical ai solutions using state-of-the-art tools and technologies.
experience : minimum 2 yrs of experience.
key responsibilities
data engineering & preprocessing
- collaborate with data scientists and engineers to source, clean, and preprocess large datasets.
- perform feature engineering and data selection to improve model inputs.
ai model development & implementation
- design, build, and validate machine learning and deep learning models, including:
- convolutional neural networks (cnns)
- recurrent neural networks (rnns/lstms)
- transformers
- nlp and computer vision models
- reinforcement learning agents
- classical ml techniques
- develop models tailored to domain-specific business challenges.
performance optimization & scalability
- optimize models for performance, latency, scalability, and resource efficiency.
- ensure models are production-ready for real-time applications.
deployment, mlops & integration
- build and maintain mlops pipelines for model deployment, monitoring, and retraining.
- use docker, kubernetes, and ci/cd tools for containerization and orchestration.
- deploy models on cloud platforms (aws, azure, gcp) or on-premise infrastructure.
- integrate models into systems and applications via apis or model-serving frameworks.
testing, validation & continuous improvement
- implement testing strategies like unit testing, regression testing, and a/b testing.
- continuously improve models based on user feedback and performance metrics.
research & innovation
- stay up to date with ai/ml advancements, tools, and techniques.
- experiment with new approaches to drive innovation and competitive advantage.
collaboration & communication
- work closely with engineers, product managers, and subject matter experts.
- document model architecture, training processes, and experimental findings.
- communicate complex technical topics to non-technical stakeholders clearly.
ethical ai practices
- support and implement ethical ai practices focusing on fairness, transparency, and accountability.
required qualifications & skills
core technical skills
- proficient in python and experienced with libraries such as tensorflow, pytorch, keras, scikit-learn.
- solid understanding of ml/dl architectures (cnns, rnns/lstms, transformers).
- skilled in data manipulation using pandas, numpy, scipy.
mlops & deployment experience
- experience with mlops tools like mlflow, kubeflow, dvc.
- familiarity with docker, kubernetes, and ci/cd pipelines.
- proven ability to deploy models on cloud platforms (aws, azure, or gcp).
software engineering & analytical thinking
- strong foundation in software engineering: git, unit testing, and code optimization.
- strong analytical mindset with experience working with large datasets.
communication & teamwork
- excellent communication skills, both written and verbal.
- collaborative team player with experience in agile environments.
preferred
advanced ai & llm expertise
- hands-on experience with llms (e.g., gpt, claude, mistral, llama).
- familiarity with prompt engineering and retrieval-augmented generation (rag).
- experience with langchain, llamaindex, and hugging face transformers.
- understanding of vector databases (e.g., pinecone, faiss, weaviate).
domain-specific experience
- experience applying ai in sectors like healthcare, finance, retail, manufacturing, or customer service.
- specialized knowledge in nlp, computer vision, or reinforcement learning.
academic & research background
- strong background in statistics and optimization.
- research publications in top ai/ml conferences (e.g., neurips, icml, cvpr, acl) are a plus.
